From 88b0504bdb945b71d6e6128fbf7a973ed70ed871 Mon Sep 17 00:00:00 2001 From: Jacky Behrendt Date: Sat, 7 Feb 2026 10:55:15 +0100 Subject: [PATCH 1/4] Add arc-ERBM, arc-EBM, and example notebook --- docs/references.bib | 12 + .../constrained_ICA_example.ipynb | 523 ++++++++ src/cedalion/sigdecomp/multimodal/arc_ebm.py | 1191 +++++++++++++++++ src/cedalion/sigdecomp/multimodal/arc_erbm.py | 919 +++++++++++++ src/cedalion/sigdecomp/unimodal/ica_ebm.py | 54 +- src/cedalion/sigdecomp/unimodal/ica_erbm.py | 26 +- 6 files changed, 2675 insertions(+), 50 deletions(-) create mode 100644 examples/machine_learning/constrained_ICA_example.ipynb create mode 100644 src/cedalion/sigdecomp/multimodal/arc_ebm.py create mode 100644 src/cedalion/sigdecomp/multimodal/arc_erbm.py diff --git a/docs/references.bib b/docs/references.bib index af906259..ef65f5fb 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -427,3 +427,15 @@ @article{Schaefer2018 year={2018}, publisher={Oxford University Press} } + + +@article{yang2025flexible, + title={A Flexible Constrained ICA Approach for Multisubject fMRI Analysis}, + author={Yang, Hanlu and Vu, Trung and Dhrubo, Ehsan Ahmed and Calhoun, Vince D and Adali, T{\"u}lay}, + journal={International Journal of Biomedical Imaging}, + volume={2025}, + number={1}, + pages={2064944}, + year={2025}, + publisher={Wiley Online Library} +} \ No newline at end of file diff --git a/examples/machine_learning/constrained_ICA_example.ipynb b/examples/machine_learning/constrained_ICA_example.ipynb new file mode 100644 index 00000000..7028b10a --- /dev/null +++ b/examples/machine_learning/constrained_ICA_example.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6e3f1d9b", + "metadata": {}, + "outputs": [], + "source": [ + "# This cells setups the environment when executed in Google Colab.\n", + "try:\n", + " import google.colab\n", + " !curl -s https://raw.githubusercontent.com/ibs-lab/cedalion/dev/scripts/colab_setup.py -o colab_setup.py\n", + " # Select branch with --branch \"branch name\" (default is \"dev\")\n", + " %run colab_setup.py\n", + "except ImportError:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5e7afeda", + "metadata": {}, + "outputs": [], + "source": [ + "import cedalion\n", + "import cedalion.sigproc.quality as quality\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import scipy as sp\n", + "import xarray as xr\n", + "from cedalion import units\n", + "from cedalion.sigdecomp.multimodal import arc_ebm, arc_erbm" + ] + }, + { + "cell_type": "markdown", + "id": "27652e3b", + "metadata": {}, + "source": [ + "# Constrained Independent Component Analysis (ICA)\n", + "\n", + "In this notebook, we demonstrate how constrained ICA methods can be applied to separate physiological sources from resting-state fNIRS data using auxiliary measurements. Specifically, we focus on adaptive-reverse constrained ICA-ERBM (arc-ERBM) and adaptive-reverse constrained ICA-EBM (arc-EBM).\n", + "\n", + "arc-ERBM and arc-EBM are constrained versions of the methods [Independent Component Analysis by Entropy Rate Bound Minimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6845364) (ICA-ERBM) and [by Entropy Bound Minimization](https://ieeexplore.ieee.org/abstract/document/5499122) (ICA-EBM). The general assumption in [Independent Component Analysis](https://en.wikipedia.org/wiki/Independent_component_analysis) is that the dataset $X \\in \\mathbb R^{N\\times T}$, with $N$ channels and $T$ sample points, is generated from a set of independent latent sources $S \\in \\mathbb R^{N\\times T}$, mixed by an unknown mixing matrix $A \\in \\mathbb R^{N \\times N}$.\n", + "\n", + "$$\n", + "X = A \\cdot S.\n", + "$$\n", + "\n", + "ICA methods aim to undo this mixing by determining a demixing matrix $W \\in \\mathbb{R}^{N \\times N}$, such that the estimated underlying sources $Y = W \\cdot X$ are maximally independent. The optimization of the demixing matrix is based on minimizing the mutual information $I$ in the case of ICA-EBM, and the mutual information rate $I_r$ in the case of ICA-ERBM. In both methods, this is done by minimizing a cost function $J$ that is equivalent to either $I$ or $I_r$ for each row vector $w_n$, $n = 1, ..., N$.\n", + "\n", + "In the constrained methods arc-EBM and arc-ERBM, we assume that there are $M \\leq N$ reference signals $r_n$, $n = 1, ..., M$, that correspond to $M$ latent sources. For each source estimate $y_n = w_n^T X$ that corresponds to a reference signal, the minimization of the cost function $J$ is extended through a constraint that uses a reference signal $r_n$:\n", + "\n", + "$$\n", + "\\min_{w_n} J(w_n) \\quad \\text{s.t.} \\quad \\varepsilon(r_n, y_n) \\geq \\rho_n\n", + "$$\n", + "\n", + "Here, $\\varepsilon$ is a similarity measure that operates in the frequency domain and enforces similar spectral characteristics between the source estimate $y_n$ and the reference signal $r_n$.\n", + "\n", + "In the following example, $X$ represents our resting-state fNIRS data, and as reference signals $r_n$, we use auxiliary PPG, respiration, and mean arterial pressure (MAP) measurements. After applying the constrained ICA methods and obtaining $W$, we can compute estimates of the separated sources as $y_n = w_n^T X$.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "76b4ea3e", + "metadata": {}, + "source": [ + "## Loading Resting-State fNIRS Data\n", + "\n", + "We load the resting-state fNIRS data, including the auxiliary physiological measurements from the SNIRF file. For the demixing problem, we use middle-distance channels of approximately 15 mm in length to ensure that physiological noise signals are present in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "29062793", + "metadata": {}, + "outputs": [], + "source": [ + "# Load data\n", + "file_path = cedalion.data.get('spafNIRS_example_sub179.snirf')\n", + "rec = cedalion.io.read_snirf(file_path)[0]\n", + " \n", + "# Read fnirs data \n", + "fnirs_amp = rec['amp']\n", + "\n", + "\n", + "# Define middle distance channels \n", + "middle_channels = ['S1D7', 'S1D8', 'S1D13', 'S1D14', 'S1D15', 'S1D16', 'S2D8', 'S2D11', 'S2D12', \n", + " 'S3D7', 'S3D9', 'S3D10', 'S4D1', 'S4D5', 'S4D10', 'S4D16', 'S5D4', 'S5D5', 'S5D11', \n", + " 'S5D15', 'S6D3', 'S6D6', 'S6D12', 'S6D14', 'S7D2', 'S7D6', 'S7D9', 'S7D13', 'S8D22', \n", + " 'S8D23', 'S8D24', 'S8D29', 'S8D30', 'S8D31', 'S9D24', 'S9D27', 'S9D28', 'S10D23', 'S10D25',\n", + " 'S10D26', 'S11D19', 'S11D26', 'S11D31', 'S12D18', 'S12D19', 'S12D22', 'S12D28', 'S13D17', \n", + " 'S13D20', 'S13D27', 'S13D29', 'S14D20', 'S14D21', 'S14D25', 'S14D30']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "92879701", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot three middle distance channels\n", + "fig, ax = plt.subplots(3, 1, sharex=True, figsize=(10, 5))\n", + "for i, ch in enumerate(['S1D7', 'S1D8', 'S1D13']):\n", + " ax[i].plot(fnirs_amp.time, fnirs_amp.sel(channel=ch, wavelength=\"760\"), \"r-\", label=\"760nm\")\n", + " ax[i].plot(fnirs_amp.time, fnirs_amp.sel(channel=ch, wavelength=\"850\"), \"b-\", label=\"850nm\")\n", + " ax[i].set_title(f\"Channel {ch}\")\n", + "\n", + "ax[0].legend()\n", + "ax[2].set_xlim(2400,2500)\n", + "ax[2].set_xlabel(\"Time (seconds)\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "8067fcc6", + "metadata": {}, + "source": [ + "## Conversion to Optical Density" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b520c07c", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert to OD\n", + "fnirs_od = cedalion.nirs.cw.int2od(fnirs_amp)" + ] + }, + { + "cell_type": "markdown", + "id": "01f689ae", + "metadata": {}, + "source": [ + "## Select Resting-State Session\n", + "\n", + "Our data contain a resting-state session of 75 seconds. We select this session and crop the first 10 seconds to remove non-stationarities in the data. From the remaining session, we select a 60-second interval for our analysis using the middle-distance channels." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "07e3caeb", + "metadata": {}, + "outputs": [], + "source": [ + "# Select the onset of the resting state interval (pre_sitting) \n", + "onset_resting = rec.stim[rec.stim.trial_type == 'pre_sitting'].onset.values[0]\n", + "\n", + "# We cropp the first 10 seconds of the resting state interval to \n", + "# avoid transient effects and select a 60 second interval for the analysis.\n", + "interval = [onset_resting + 10, onset_resting + 70]\n", + "\n", + "# Select interval and channels \n", + "interval_fnirs_od = fnirs_od.sel(time=slice(interval[0], interval[1]))\n", + "interval_fnirs_od = interval_fnirs_od.sel(channel= middle_channels)" + ] + }, + { + "cell_type": "markdown", + "id": "191a3a64", + "metadata": {}, + "source": [ + "## Channel Quality Assessment and Pruning\n", + "\n", + "We compute the Scalp Coupling Index (SCI) and Peak Spectral Power (PSP) for channel quality assessment. SCI and PSP are computed for each channel, and we then select the 40 channels with the highest percentage of clean time." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "763332d1", + "metadata": {}, + "outputs": [], + "source": [ + "# Define parameters for quality metrics\n", + "window_length = 5 * units.s\n", + "sci_thresh = 0.75\n", + "psp_thresh = 0.1\n", + "sci_psp_percentage_thresh = 0.75\n", + "\n", + "# Compute SCI and PSP \n", + "sci, sci_mask = quality.sci(interval_fnirs_od, window_length, sci_thresh)\n", + "psp, psp_mask = quality.psp(interval_fnirs_od, window_length, psp_thresh)\n", + "sci_x_psp_mask = sci_mask & psp_mask\n", + "perc_time_clean = sci_x_psp_mask.sum(dim=\"time\") / len(sci.time)\n", + "\n", + "# Set the number of channels to include in the ICA analysis\n", + "num_ch = 40\n", + "\n", + "# Select the best channels \n", + "id_best_channels = np.argsort(perc_time_clean)[-num_ch:]\n", + "best_channels = id_best_channels['channel']\n", + "best_middle_channels = interval_fnirs_od.sel(channel=best_channels)" + ] + }, + { + "cell_type": "markdown", + "id": "9a0d3ade", + "metadata": {}, + "source": [ + "## Convert Optical Density to Concentration Changes " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b42fd6ad", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert optical density to concentration changes \n", + "montage = rec.geo3d\n", + "dpf = xr.DataArray(\n", + " [6, 6],\n", + " dims=\"wavelength\",\n", + " coords={\"wavelength\": fnirs_od.wavelength},)\n", + " \n", + "fnirs_con = cedalion.nirs.cw.od2conc(fnirs_od, montage, dpf)" + ] + }, + { + "cell_type": "markdown", + "id": "4aa379df", + "metadata": {}, + "source": [ + "## High-Pass Filtering and Selection of HbO" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "79b33142", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/cedalion/lib/python3.11/site-packages/xarray/core/variable.py:315: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", + " data = np.asarray(data)\n" + ] + } + ], + "source": [ + "# Apply high-pass filter\n", + "y_filt = fnirs_con.cd.freq_filter(fmin= 0.01, fmax= 0, butter_order=4)\n", + "\n", + "# Select resting state interval\n", + "y_filt = y_filt.sel(time = slice(interval[0], interval[1]))\n", + "\n", + "# Select middle distance channels\n", + "y_filt = y_filt.sel(channel=best_middle_channels.channel.values)\n", + "\n", + "# Select only HbO signal \n", + "y_filt = y_filt.sel(chromo = 'HbO')\n", + "\n", + "# Turn to numpy array \n", + "data = y_filt.values" + ] + }, + { + "cell_type": "markdown", + "id": "0cff03d6", + "metadata": {}, + "source": [ + "## Prepare the Auxiliary Signals\n", + "\n", + "We now extract the respiration ('Resp'), PPG ('Pleth'), or mean arterial pressure ('MAP') signals from the recording. These signals must be downsampled to match the fNIRS sampling frequency. We first select the resting-state interval with an additional buffer and apply a band-pass filter to the data to avoid aliasing effects. The MAP signal may contain missing samples, which we address using an interpolation step." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d4fca2e0", + "metadata": {}, + "outputs": [], + "source": [ + "# Select the auxiliary signal from the recording \n", + "aux_name = 'Resp' # use 'MAP', 'Pleth' or 'Resp' \n", + "aux_signal = rec.aux_ts[aux_name]\n", + "\n", + "# Select the interval of the auxiliary signal with a 100 second buffer \n", + "# before and after the resting state interval to avoid edge effects in the filtering step\n", + "buffer = 100 \n", + "aux_signal = aux_signal.sel(time = slice(interval[0]- buffer, interval[1] + buffer ))\n", + "\n", + "# Add a new coordinate called samples and add unit \n", + "aux_signal['time'].attrs['units'] = 'seconds'\n", + "samples = np.arange(aux_signal.sizes['time'])\n", + "aux_signal = aux_signal.assign_coords(samples=('time', samples))\n", + "\n", + "# Fix missing samples in the MAP signal \n", + "if aux_name == 'MAP':\n", + " aux_signal = aux_signal.interpolate_na(dim = 'time' ,method = 'cubic',\n", + " fill_value='extrapolate')\n", + "\n", + "# Apply bandpass filter to the auxiliary signal to avaoid aliasing effects after the downsampling step.\n", + "aux_signal = aux_signal.cd.freq_filter(fmin= 0.01, fmax= 2.5 , butter_order=4) \n", + "\n", + "# Downsample the auxiliary signal by interpolating it to the time points of the fNIRS signal\n", + "time_line = fnirs_con.sel(time = slice(interval[0]- buffer,interval[1]+buffer))\n", + "aux_signal = aux_signal.drop_duplicates(dim='time')\n", + "aux_signal = aux_signal.interp(time=time_line.time)\n", + "aux_signal = aux_signal.dropna(dim=\"time\", how=\"any\")\n", + "\n", + "# Remove buffer \n", + "aux_signal = aux_signal.sel(time = slice(interval[0], interval[1]))\n", + "\n", + "# Turn to numpy array and reshape \n", + "aux_signal = np.array(aux_signal.values, dtype=np.float64).T\n", + "aux_signal = aux_signal.reshape(1, -1) \n" + ] + }, + { + "cell_type": "markdown", + "id": "861ab352", + "metadata": {}, + "source": [ + "## Z-Transform Normalization" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c751fe84", + "metadata": {}, + "outputs": [], + "source": [ + "# z-transform the data and auxiliary signal\n", + "data = sp.stats.zscore(data, axis=1) \n", + "aux_signal = sp.stats.zscore(aux_signal, axis=1) " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "739b1540", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data and the auxiliary signal\n", + "fig, ax = plt.subplots(2, 1, figsize=(10, 5))\n", + "\n", + "x_time = np.arange(data.shape[1]) * 1/(7.4)\n", + "ax[0].plot(x_time, data.T)\n", + "ax[0].set_title('fNIRS data (HbO)')\n", + "ax[1].plot(x_time, aux_signal[0])\n", + "ax[1].set_title(f'Auxiliary signal ({aux_name})')\n", + "ax[1].set_xlabel('Time (seconds)')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8a9159f2", + "metadata": {}, + "source": [ + "## Apply Constrained ICA Methods\n", + "\n", + "We define a frequency reference signal by computing the power spectral density of the reference signal. We then apply the constrained ICA methods to the data." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "dbccf84b", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the time domain and frequency domain reference signals \n", + "ref = np.copy(aux_signal)\n", + "ref_psd = (2/ ref.shape[1] ) * np.abs(np.fft.rfft(ref, axis = 1 )**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e21ae032", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the filter length for arc-ERBM \n", + "p = 11\n", + "\n", + "# Apply ICA methods \n", + "W1 = arc_erbm.arc_erbm(data, ref_psd.T, p)\n", + "W2 = arc_erbm.arc_erbm(data, ref_psd.T, p, ref.T)\n", + "W3 = arc_ebm.arc_ebm(data, ref_psd.T, 'psd')" + ] + }, + { + "cell_type": "markdown", + "id": "744d6ba9", + "metadata": {}, + "source": [ + "## Compute Source Estimates\n", + "\n", + "For each constrained method, the first row of the demixing matrix corresponds to the referenced source. We therefore select the first row and compute the source estimate as $y = w_0^T X$." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "862fee83", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the estimated sources\n", + "source_arc_erbm = W1[0].dot(data)\n", + "source_arc_erbm_pr = W2[0].dot(data)\n", + "source_arc_ebm = W3[0].dot(data)\n", + "\n", + "# z-transform the estimated sources\n", + "source_arc_erbm = sp.stats.zscore(source_arc_erbm)\n", + "source_arc_erbm_pr = sp.stats.zscore(source_arc_erbm_pr)\n", + "source_arc_ebm = sp.stats.zscore(source_arc_ebm)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1c1c083e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot source estimates and reference signal\n", + "fig, ax = plt.subplots(3, 1, figsize=(10, 7))\n", + "\n", + "estimates = [source_arc_erbm, source_arc_erbm_pr, source_arc_ebm]\n", + "labels = ['arc-ERBM estimate', 'arc-ERBM (PR) estimate', 'arc-EBM estimate']\n", + "for i in range(3):\n", + " # Compute peak cross correlation for +/- 2 seconds lag \n", + " lags = np.arange(-15, 16, 1)\n", + " cross_corr = [np.corrcoef(ref, np.roll(estimates[i], lag, axis=0))[0, 1] for lag in lags]\n", + " max_corr = np.max(np.abs(cross_corr))\n", + " best_lag = lags[np.argmax(np.abs(cross_corr))]\n", + "\n", + " # Copmute RMSE between reference and estimate \n", + " rmse = np.sqrt(np.mean((ref - np.roll(estimates[i], best_lag, axis=0))**2)) \n", + " ax[i].set_title(f'{labels[i]} (correlation with reference: {max_corr:.2f}, RMSE: {rmse:.2f} )', \n", + " fontsize=10)\n", + "\n", + " # Plot estimate and reference \n", + " signal = np.roll(estimates[i], best_lag, axis=0)\n", + " signal = np.sign(np.corrcoef(ref, signal)[0, 1]) * signal\n", + " ax[i].plot(x_time, signal, label = labels[i])\n", + " ax[i].plot(x_time, ref.T, label = 'Reference signal', alpha = 0.7)\n", + " ax[i].legend( loc='upper left', bbox_to_anchor=(1, 1))\n", + "\n", + "\n", + "ax[2].set_xlabel('Time (seconds)')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cedalion", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/cedalion/sigdecomp/multimodal/arc_ebm.py b/src/cedalion/sigdecomp/multimodal/arc_ebm.py new file mode 100644 index 00000000..d2f7ef40 --- /dev/null +++ b/src/cedalion/sigdecomp/multimodal/arc_ebm.py @@ -0,0 +1,1191 @@ +"""Independent Component Analysis by Entropy Bound Minimization (ICA-EBM). + +This code is based on :cite:t:`Li2010A` and converted matlab versions provided by the +MLSP Lab at the University of Maryland, which is available here: +https://mlsp.umbc.edu/resources.html. + + +(:cite:t:`yang2025flexible`) H. Yang, T. Vu, Ehsan Ahmed Dhrubo, V. D. Calhoun, and Tülay Adali, +“A Flexible Constrained ICA Approach for Multisubject fMRI Analysis,” +International Journal of Biomedical Imaging, vol. 2025, no. 1, Jan. 2025, +doi: https://doi.org/10.1155/ijbi/2064944. +""" + + +import numpy as np +import cedalion.data + +def arc_ebm(X: np.ndarray, guess_mat, constraint = 'correlation') -> np.ndarray: + """Adaptive-reverse Constrained ICA by Entropy Bound Minimization (arc-EBM) is a constrained ICA algorithm. + arc-EBM calculates the blind source separation demixing matrix corresponding to X, + using the reference signals in guess_mat and the constraint specified by constraint. + + Args: + X (np.ndarray, (Channels, Time Points)): the [N x T] input multivariate time series with dimensionality N observations/channels and T time points + guess_mat (np.ndarray, (Time Points, Referenced Channels)), (np.ndarray, (Time Points/2, Referenced Channels)): Time or frequency domain reference signals. The number of reference signals should be less than or equal to the number of channels in X. The first dimension should be T for time domain signals and T/2 for frequency domain signals. + constraint (str): the constraint to be used for the gradient step, either 'correlation' (default) or 'psd' + + Returns: + W (np.ndarray, (Channels, Channels)): the [N x N] demixing matrix with weights for N channels/sources. + To obtain the independent components, the demixed signals can be calculated as S = W @ X. + + Initial Contributors: + - Jacqueline Behrendt | j.behrendt@tu-berlin.de | 2026 + + References: + This code is based on the matlab version by Xi-Lin Li (:cite:t:`Li2010A`) + Xi-Lin Li and Tulay Adali, "Independent component analysis by entropy bound minimization," + IEEE Trans. Signal Processing, vol. 58, no. 10, pp. 5151-5164, Oct. 2010. + + (:cite:t:`yang2025flexible`) H. Yang, T. Vu, Ehsan Ahmed Dhrubo, V. D. Calhoun, and Tülay Adali, + “A Flexible Constrained ICA Approach for Multisubject fMRI Analysis,” + International Journal of Biomedical Imaging, vol. 2025, no. 1, Jan. 2025, + doi: https://doi.org/10.1155/ijbi/2064944. + + """ + + ############################################################################################################### + # Part 0: Preprocessing + ############################################################################################################### + + rho = np.arange(0, 1.01, 0.01) + max_iter_fastica = 100 + max_iter_orth = 1000 + max_iter_orth_refine = 1000 + max_iter_nonorth = 1000 + saddle_test_enable = True + tolerance = 1e-6 + max_cost_increase_number = 5 + stochastic_search_factor = 1 + eps = np.finfo(np.float64).eps + gam = 100 + + # report the progress if verbose == True + verbose = False + + # Load 8 measuring functions. But we only use 4 of them. + K = 8 + file_path = cedalion.data.get("measfunc_table.npy") + table = np.load(file_path, allow_pickle=True) + + nf1, nf3, nf5, nf7 = table[0], table[2], table[4], table[6] + + N = X.shape[0] # number of channels + T = X.shape[1] # number of time points + X, P = pre_processing(X) + + # Define the epsilon function based on the constraint + if constraint == 'correlation': + def epsilon(a,b): + # correlation coefficient + return np.corrcoef(a, b)[0, 1] + + def epsilon_grad(): + # gradient for correlation constraint + mu_signed[n] = np.sign(e_pair) * mu_c[n] + c_grad = mu_signed[n] * (X.dot(r_n_c) ) * (1/np.sqrt(T)) + return np.reshape(c_grad, (-1, 1)) + + + if constraint == 'psd': + # compute psd of X + X_hat = (2/T) * np.fft.rfft(X, axis = 1) + + # compute cross psd of X + C_hat = np.zeros((X_hat.shape[1], N , N ), dtype=complex) + C_hat = (X_hat[:, None, :] * np.conjugate(X_hat[None, :, :])).transpose(2, 0, 1) + + # center C_hat + C_hat_mean = np.mean(C_hat, axis = 0) + C_hat = C_hat - np.reshape(C_hat_mean, (1, N, N)) + + # store real matrix for gradient computation + C_tilde = np.real(C_hat + np.transpose(C_hat, (0, 2, 1)) ) + + # define correlation between psd of estimated source and reference psd + def epsilon(a,b): + # power spectral density + # compute psd for real signal a + # b is already a psd + psd_a = (2/T) * np.abs(np.fft.rfft(a))**2 + psd_correlation = np.corrcoef(psd_a, b)[0,1] + return psd_correlation + + # define dot product between psd of estimated source and reference psd + def epsilon_dot(a,b): + # abs of dot product + a = a / np.linalg.norm(a) + psd_a = (2/T) * np.abs(np.fft.rfft(a))**2 + psd_a = psd_a - np.mean(psd_a) + b = b - np.mean(b) + b = b / np.linalg.norm(b) + abs_dot = np.abs(np.dot(psd_a, b)) + return abs_dot + + # define gradient function for psd constraint + def epsilon_grad(): + # compute gradient of epsilon for the psd constraint + psd_s = (2/T) * np.abs(np.fft.rfft(w.T.dot(X)))**2 + sign = np.sign(np.corrcoef(psd_s, r_n_c)[0,1]) + r = np.reshape(r_n_c, (-1, 1, 1)) + c_grad = sign * mu_c[n] * np.sum( np.multiply(np.dot(C_tilde, w), r), axis = 0 ) + return c_grad + + + # make initial guess for demixing matrix W + W = np.random.rand(N, N) + + # symmetric decorrelation + W = symdecor(W) + + num_guess = guess_mat.shape[1] # number of reference signals + mu_c = np.ones((num_guess, 1)) + corr_w_guess = np.zeros((num_guess, N)) + num_W = np.shape(W)[0] + corr_w_guess = np.zeros((num_guess, num_W)) + + # resort W based on correlation with reference signals + for kl in range(num_guess): + r_n_c = guess_mat[:, kl] + for lp in range(num_W): + w = W[lp, :].T + corr_w_guess[kl, lp] = epsilon(X.T.dot(w), r_n_c) + + + # may need auction to auction to choose order + max_index = np.argmax(np.abs(corr_w_guess), axis = 1) + if len(np.unique(max_index)) != num_guess: + colsol, _ = auction((1- np.abs(corr_w_guess)).T) + max_index = colsol.T + + c = np.arange(0, num_W) + c = np.setdiff1d(c, max_index) + sort_order = np.concatenate((max_index, c)) + W = W[sort_order, :] + + last_W = np.copy(W) + best_W = np.copy(W) + Cost = np.zeros((max_iter_fastica, 1)) + min_cost = np.inf + cost_increaser_counter = 0 + negentropy_array = np.zeros((N,1 )) + + + for iter in range(max_iter_fastica): + Y = np.copy(W.dot(X)) + for n in range(N): + y = np.copy(Y[n, :]) + # evaluate the upper bound of negentropy of the nth component + NE_Bound = np.zeros((K, 1)) + EGx = np.zeros((K, 1)) + # we only need to calculate these quantities once + yy = y* y + sign_y = np.sign(y) + abs_y = np.abs(y) + inv_pabs_y = 1/(1 + abs_y) + inv_pabs_yy = 1/(1+ yy) + inv_p10abs_y = 1/(10+abs_y) + + # G1(x) = x^4 + EGx[0] = np.sum(yy*yy)/T + if EGx[0] < nf1['min_EGx']: + NE_Bound[0] = simplified_ppval(nf1['pp_slope'], nf1['min_EGx'] ) * (EGx[0] - nf1['min_EGx']) + NE_Bound[0] = simplified_ppval(nf1['pp'],nf1['min_EGx']) + np.abs(NE_Bound[0] ) + else: + if EGx[0] > nf1['max_EGx']: + NE_Bound[0] = 0 + else: + NE_Bound[0] = simplified_ppval(nf1['pp'], EGx[0] ) + + # G3(x) = np.abs(x)/ (1 + np.abs(x)) + EGx[2] = 1 - np.sum(inv_pabs_y)/T + if EGx[2] < nf3['min_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['min_EGx'] ) * (EGx[2] - nf3['min_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['min_EGx']) + np.abs(NE_Bound[2]) + else: + if EGx[2] > nf3['max_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['max_EGx'] ) * (EGx[2] - nf3['max_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['max_EGx']) + np.abs(NE_Bound[2]) + + else: + NE_Bound[2] = simplified_ppval(nf3['pp'], EGx[2] ) + + # G5(x) = x* np.abs(x) /(10 + np.abs(x)) + EGx[4] = np.sum( y * abs_y * inv_p10abs_y )/T + if EGx[4] < nf5['min_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['min_EGx'] ) * (EGx[4] - nf5['min_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['min_EGx']) + np.abs(NE_Bound[4]) + else: + if EGx[4] > nf5['max_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['max_EGx'] ) * (EGx[4] - nf5['max_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['max_EGx']) + np.abs(NE_Bound[4]) + else: + NE_Bound[4] = simplified_ppval(nf5['pp'], EGx[4] ) + + # G7(x) = x / (1 + x**2) + EGx[6] = np.sum(y*inv_pabs_yy)/T + if EGx[6] < nf7['min_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['min_EGx'] ) * (EGx[6] - nf7['min_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['min_EGx']) + np.abs(NE_Bound[6]) + else: + if EGx[6] > nf7['max_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['max_EGx'] ) * (EGx[6] - nf7['max_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['max_EGx']) + np.abs(NE_Bound[6]) + else: + NE_Bound[6] = simplified_ppval(nf7['pp'], EGx[6] ) + + # select the tightest upper bound + max_NE, max_i = np.max(NE_Bound), np.argmax(NE_Bound) + negentropy_array[n] = np.copy(max_NE) + Cost[iter] = np.copy(Cost[iter] - max_NE) + + + if Cost[iter] < min_cost: + min_cost = np.copy(Cost[iter]) + best_W = np.copy(last_W) + cost_increaser_counter = 0 + else: + cost_increaser_counter = cost_increaser_counter + 1 + + W = np.multiply(np.multiply(Y, Y), Y).dot(X.T) / T - 3 * W + W = symdecor(W) + + + if 1 - np.min(np.abs(np.diag(W.dot(last_W.T)))) < tolerance: + break + else : + last_W = np.copy(W) + if cost_increaser_counter > max_cost_increase_number: + break + + W = np.copy(best_W) +############################################################################################################## +# Part 1: Orthogonal ICA +# varying step size, stochastic gradient search +############################################################################################################## + + if verbose: + print('Orthogonal ICA stage.') + + + # resort existing W based on correlation with reference signals + for kl in range(num_guess): + r_n_c = guess_mat[:, kl] + for lp in range(num_W): + w = W[lp, :].T + corr_w_guess[kl, lp] = epsilon(X.T.dot(w), r_n_c) + + # may need auction to auction to choose order + max_index = np.argmax(np.abs(corr_w_guess), axis = 1) + + if len(np.unique(max_index)) != num_guess: + colsol, _ = auction((1- np.abs(corr_w_guess)).T) + max_index = colsol.T + + c = np.arange(0, num_W) + c = np.setdiff1d(c, max_index) + sort_order = np.concatenate((max_index, c)) + + W = W[sort_order, :] + + + last_W = np.copy(W) + best_W = np.copy(W) + Cost = np.zeros((max_iter_orth, 1)) + min_cost = np.inf + min_cost_queue = np.copy(min_cost* np.ones((max_iter_orth, 1))) + mu = 1/6.25 + min_mu = 1/50 + cost_increaser_counter = 0 + fastica_on = True + max_negentropy = np.zeros((N, 1)) + negentropy_array = np.zeros((N, 1)) + + for iter in range(max_iter_orth): + Y = np.copy(W.dot(X)) + for n in range(N): + w = np.copy(W[n, :].T) + y = np.copy(Y[n, :] ) + + # evaluate the upper bound of negentropy of the nth component + NE_Bound = np.zeros((K, 1)) + EGx = np.zeros((K, 1)) + # we only need to calculate these quantities once + yy = y* y + sign_y = np.sign(y) + abs_y = np.abs(y) + inv_pabs_y = 1/(1 + abs_y) + inv_pabs_yy = 1/(1+ yy) + inv_p10abs_y = 1/(10+abs_y) + + # G1(x) = x^4 + EGx[0] = np.sum(yy*yy)/T + if EGx[0] < nf1['min_EGx']: + NE_Bound[0] = simplified_ppval(nf1['pp_slope'], nf1['min_EGx'] ) * (EGx[0] - nf1['min_EGx']) + NE_Bound[0] = simplified_ppval(nf1['pp'],nf1['min_EGx']) + np.abs(NE_Bound[0] ) + else: + if EGx[0] > nf1['max_EGx']: + NE_Bound[0] = 0 + else: + NE_Bound[0] = simplified_ppval(nf1['pp'], EGx[0] ) + + # G3(x) = np.abs(x)/ (1 + np.abs(x)) + EGx[2] = 1 - np.sum(inv_pabs_y)/T + if EGx[2] < nf3['min_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['min_EGx'] ) * (EGx[2] - nf3['min_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['min_EGx']) + np.abs(NE_Bound[2]) + else: + if EGx[2] > nf3['max_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['max_EGx'] ) * (EGx[2] - nf3['max_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['max_EGx']) + np.abs(NE_Bound[2]) + + else: + NE_Bound[2] = simplified_ppval(nf3['pp'], EGx[2] ) + + # G5(x) = x* np.abs(x) /(10 + np.abs(x)) + EGx[4] = np.sum( y * abs_y * inv_p10abs_y )/T + if EGx[4] < nf5['min_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['min_EGx'] ) * (EGx[4] - nf5['min_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['min_EGx']) + np.abs(NE_Bound[4]) + else: + if EGx[4] > nf5['max_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['max_EGx'] ) * (EGx[4] - nf5['max_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['max_EGx']) + np.abs(NE_Bound[4]) + else: + NE_Bound[4] = simplified_ppval(nf5['pp'], EGx[4] ) + + # G7(x) = x / (1 + x**2) + EGx[6] = np.sum(y*inv_pabs_yy)/T + if EGx[6] < nf7['min_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['min_EGx'] ) * (EGx[6] - nf7['min_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['min_EGx']) + np.abs(NE_Bound[6]) + else: + if EGx[6] > nf7['max_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['max_EGx'] ) * (EGx[6] - nf7['max_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['max_EGx']) + np.abs(NE_Bound[6]) + else: + NE_Bound[6] = simplified_ppval(nf7['pp'], EGx[6] ) + + # select the tightest upper bound + max_NE, max_i = np.max(NE_Bound), np.argmax(NE_Bound) + negentropy_array[n] = np.copy(max_NE) + Cost[iter] = np.copy(Cost[iter] - max_NE) + + if ~fastica_on: + weight = np.random.rand(1, T) + + # Perform orthogonal ICA + if max_i == 0: + # G1(x) = x^4 + if fastica_on : + grad = X.dot( (4* y* yy).T )/T + Edgx = 12 + else : + grad = X.dot((4 * weight * y * yy ).T ) / np.sum(weight) + vEGx = 2 * (EGx[0] > nf1['critical_point']) -1 + elif max_i == 2: + # G3(x) = np.abs(x)/ (1 + np.abs(x)) + if fastica_on : + grad = X.dot( (sign_y * inv_pabs_y * inv_pabs_y).T )/T + Edgx = np.sum(-2 * inv_pabs_y * inv_pabs_y * inv_pabs_y)/T + else : + grad = X.dot((weight * sign_y * inv_pabs_y * inv_pabs_y).T ) / np.sum(weight) + vEGx = 2 * (EGx[2] > nf3['critical_point']) -1 + elif max_i == 4: + # G5(x) = x* np.abs(x) /(10 + np.abs(x)) + if fastica_on : + grad = X.dot((abs_y *(20 + abs_y) * inv_p10abs_y * inv_p10abs_y).T )/T + Edgx = np.sum(200 * sign_y * inv_p10abs_y * inv_p10abs_y * inv_p10abs_y)/T + else : + grad = X.dot((weight * abs_y * (20 + abs_y) * inv_p10abs_y**2 ).T ) / np.sum(weight) + vEGx = 2 * (EGx[4] > nf5['critical_point']) -1 + elif max_i == 6: + # G7(x) = x / (1 + x**2) + if fastica_on : + grad = X.dot(((1 - yy)* inv_pabs_yy**2).T )/T + Edgx = np.sum(2 * y * (yy-3)* inv_pabs_yy* inv_pabs_yy* inv_pabs_yy)/T + else : + grad = X.dot((weight * (1 - yy) * inv_pabs_yy**2 ).T ) / np.sum(weight) + vEGx = 2 * (EGx[6] > nf7['critical_point']) -1 + if fastica_on : + w1 = grad - Edgx * w + else : + grad = vEGx * grad + w = np.reshape(w, (-1, 1)) + grad = grad - ((w.T).dot(grad)) * w + grad = grad / np.linalg.norm(grad) + w1 = w + mu * grad + + W[n, :] = np.copy(w1.T) + + W = np.copy(symdecor(W)) + + if Cost[iter] < min_cost: + cost_increaser_counter = 0 + min_cost = np.copy(Cost[iter]) + best_W = np.copy(last_W) + max_negentropy = np.copy(negentropy_array) + else: + cost_increaser_counter = cost_increaser_counter + 1 + + min_cost_queue[iter] = np.copy(min_cost) + + if fastica_on : + if cost_increaser_counter >= max_cost_increase_number or 1- np.min(np.abs(np.diag(W.dot(last_W.T)))) < tolerance: + cost_increaser_counter = 0 + W = np.copy(best_W ) + last_W = np.copy(W) + fastica_on = False + continue + else : + if cost_increaser_counter > stochastic_search_factor * max_cost_increase_number: + if mu > min_mu: + cost_increaser_counter = 0 + W = np.copy(best_W ) + last_W = np.copy(W) + mu = mu/2 + continue + else: + break + last_W = np.copy(W) + + # End of Part 1 + W = np.copy(best_W) + ############################################################################################################## + # Part 2: check for saddle points + ############################################################################################################## + if saddle_test_enable : + if verbose: + print('Saddle point detection.') + SADDLE_TESTED = False + saddle_tested = True + + while saddle_tested: + saddle_tested = False + Y = np.copy(W.dot(X)) + for m in range(N): + w1 = np.copy(W[m, :].T ) + ym = np.copy(Y[m, :]) + for n in range(m+1, N): + w2 = np.copy(W[n, :].T ) + yn = np.copy(Y[n, :]) + + yr1 = (ym + yn)/ np.sqrt(2) + yr2 = (ym - yn)/ np.sqrt(2) + y = np.copy(yr1) + # we only need to calculate these quantities once + yy = y* y + sign_y = np.sign(y) + abs_y = np.abs(y) + inv_pabs_y = 1/(1 + abs_y) + inv_pabs_yy = 1/(1+ yy) + inv_p10abs_y = 1/(10+abs_y) + + # G1(x) = x^4 + EGx[0] = np.sum(yy*yy)/T + if EGx[0] < nf1['min_EGx']: + NE_Bound[0] = simplified_ppval(nf1['pp_slope'], nf1['min_EGx'] ) * (EGx[0] - nf1['min_EGx']) + NE_Bound[0] = simplified_ppval(nf1['pp'],nf1['min_EGx']) + np.abs(NE_Bound[0] ) + else: + if EGx[0] > nf1['max_EGx']: + NE_Bound[0] = 0 + else: + NE_Bound[0] = simplified_ppval(nf1['pp'], EGx[0] ) + + # G3(x) = np.abs(x)/ (1 + np.abs(x)) + EGx[2] = 1 - np.sum(inv_pabs_y)/T + if EGx[2] < nf3['min_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['min_EGx'] ) * (EGx[2] - nf3['min_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['min_EGx']) + np.abs(NE_Bound[2]) + else: + if EGx[2] > nf3['max_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['max_EGx'] ) * (EGx[2] - nf3['max_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['max_EGx']) + np.abs(NE_Bound[2]) + + else: + NE_Bound[2] = simplified_ppval(nf3['pp'], EGx[2] ) + + # G5(x) = x* np.abs(x) /(10 + np.abs(x)) + EGx[4] = np.sum( y * abs_y * inv_p10abs_y )/T + if EGx[4] < nf5['min_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['min_EGx'] ) * (EGx[4] - nf5['min_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['min_EGx']) + np.abs(NE_Bound[4]) + else: + if EGx[4] > nf5['max_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['max_EGx'] ) * (EGx[4] - nf5['max_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['max_EGx']) + np.abs(NE_Bound[4]) + else: + NE_Bound[4] = simplified_ppval(nf5['pp'], EGx[4] ) + + # G7(x) = x / (1 + x**2) + EGx[6] = np.sum(y*inv_pabs_yy)/T + if EGx[6] < nf7['min_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['min_EGx'] ) * (EGx[6] - nf7['min_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['min_EGx']) + np.abs(NE_Bound[6]) + else: + if EGx[6] > nf7['max_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['max_EGx'] ) * (EGx[6] - nf7['max_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['max_EGx']) + np.abs(NE_Bound[6]) + else: + NE_Bound[6] = simplified_ppval(nf7['pp'], EGx[6] ) + + # select the tightest upper bound + max_NE, max_i = np.max(NE_Bound), np.argmax(NE_Bound) + negentropy1 = max_NE + + y = np.copy(yr2) + # we only need to calculate these quantities once + yy = y* y + sign_y = np.sign(y) + abs_y = np.abs(y) + inv_pabs_y = 1/(1 + abs_y) + inv_pabs_yy = 1/(1+ yy) + inv_p10abs_y = 1/(10+abs_y) + + # G1(x) = x^4 + EGx[0] = np.sum(yy*yy)/T + if EGx[0] < nf1['min_EGx']: + NE_Bound[0] = simplified_ppval(nf1['pp_slope'], nf1['min_EGx'] ) * (EGx[0] - nf1['min_EGx']) + NE_Bound[0] = simplified_ppval(nf1['pp'],nf1['min_EGx']) + np.abs(NE_Bound[0] ) + else: + if EGx[0] > nf1['max_EGx']: + NE_Bound[0] = 0 + else: + NE_Bound[0] = simplified_ppval(nf1['pp'], EGx[0] ) + + # G3(x) = np.abs(x)/ (1 + np.abs(x)) + EGx[2] = 1 - np.sum(inv_pabs_y)/T + if EGx[2] < nf3['min_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['min_EGx'] ) * (EGx[2] - nf3['min_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['min_EGx']) + np.abs(NE_Bound[2]) + else: + if EGx[2] > nf3['max_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['max_EGx'] ) * (EGx[2] - nf3['max_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['max_EGx']) + np.abs(NE_Bound[2]) + + else: + NE_Bound[2] = simplified_ppval(nf3['pp'], EGx[2] ) + + # G5(x) = x* np.abs(x) /(10 + np.abs(x)) + EGx[4] = np.sum( y * abs_y * inv_p10abs_y )/T + if EGx[4] < nf5['min_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['min_EGx'] ) * (EGx[4] - nf5['min_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['min_EGx']) + np.abs(NE_Bound[4]) + else: + if EGx[4] > nf5['max_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['max_EGx'] ) * (EGx[4] - nf5['max_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['max_EGx']) + np.abs(NE_Bound[4]) + else: + NE_Bound[4] = simplified_ppval(nf5['pp'], EGx[4] ) + + # G7(x) = x / (1 + x**2) + EGx[6] = np.sum(y*inv_pabs_yy)/T + if EGx[6] < nf7['min_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['min_EGx'] ) * (EGx[6] - nf7['min_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['min_EGx']) + np.abs(NE_Bound[6]) + else: + if EGx[6] > nf7['max_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['max_EGx'] ) * (EGx[6] - nf7['max_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['max_EGx']) + np.abs(NE_Bound[6]) + else: + NE_Bound[6] = simplified_ppval(nf7['pp'], EGx[6] ) + + # select the tightest upper bound + max_NE, max_i = np.max(NE_Bound), np.argmax(NE_Bound) + negentropy2 = max_NE + + if negentropy1 + negentropy2 > max_negentropy[m] + max_negentropy[n]+ eps : + if verbose: + print('Rotationg %d and %d.' % (m, n)) + max_negentropy[m] = np.copy(negentropy1) + max_negentropy[n] = np.copy(negentropy2) + W[m, : ] = np.copy((w1+ w2).T/np.sqrt(2)) + W[n, : ] = np.copy((w1- w2).T/np.sqrt(2) ) + Y[m, :] = yr1 + Y[n, :] = yr2 + ym = yr1 + w1 = np.copy(W[m, :].T ) + saddle_tested = True + SADDLE_TESTED = True + + + else: + SADDLE_TESTED = False + + if SADDLE_TESTED : + ############################################################################################################## + # Part 3: if saddle points are detected, refine orthogonal ICA + # fix step size gradient search + ############################################################################################################## + if verbose: + print('Orthogonal ICA refinement is required because saddle points are detected.') + last_W = np.copy(W) + best_W = np.copy(W) + Cost = np.zeros((max_iter_orth_refine, 1)) + min_cost = np.inf + min_cost_queue = min_cost * np.ones((max_iter_orth_refine, 1)) + mu = 1/ 50 + cost_increaser_counter = 0 + fastica_on = True + + for iter in range(max_iter_orth_refine): + for n in range(N): + w = np.copy(W[n, :].T) + y = np.copy(w.T.dot(X)) + # evaluate the upper bound of negentropy of the nth component + NE_Bound = np.zeros((K, 1)) + EGx = np.zeros((K, 1)) + # we only need to calculate these quantities once + yy = y* y + sign_y = np.sign(y) + abs_y = np.abs(y) + inv_pabs_y = 1/(1 + abs_y) + inv_pabs_yy = 1/(1+ yy) + inv_p10abs_y = 1/(10+abs_y) + + # G1(x) = x^4 + EGx[0] = np.sum(yy*yy)/T + if EGx[0] < nf1['min_EGx']: + NE_Bound[0] = simplified_ppval(nf1['pp_slope'], nf1['min_EGx'] ) * (EGx[0] - nf1['min_EGx']) + NE_Bound[0] = simplified_ppval(nf1['pp'],nf1['min_EGx']) + np.abs(NE_Bound[0] ) + else: + if EGx[0] > nf1['max_EGx']: + NE_Bound[0] = 0 + else: + NE_Bound[0] = simplified_ppval(nf1['pp'], EGx[0] ) + + # G3(x) = np.abs(x)/ (1 + np.abs(x)) + EGx[2] = 1 - np.sum(inv_pabs_y)/T + if EGx[2] < nf3['min_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['min_EGx'] ) * (EGx[2] - nf3['min_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['min_EGx']) + np.abs(NE_Bound[2]) + else: + if EGx[2] > nf3['max_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['max_EGx'] ) * (EGx[2] - nf3['max_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['max_EGx']) + np.abs(NE_Bound[2]) + + else: + NE_Bound[2] = simplified_ppval(nf3['pp'], EGx[2] ) + + # G5(x) = x* np.abs(x) /(10 + np.abs(x)) + EGx[4] = np.sum( y * abs_y * inv_p10abs_y )/T + if EGx[4] < nf5['min_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['min_EGx'] ) * (EGx[4] - nf5['min_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['min_EGx']) + np.abs(NE_Bound[4]) + else: + if EGx[4] > nf5['max_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['max_EGx'] ) * (EGx[4] - nf5['max_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['max_EGx']) + np.abs(NE_Bound[4]) + else: + NE_Bound[4] = simplified_ppval(nf5['pp'], EGx[4] ) + + # G7(x) = x / (1 + x**2) + EGx[6] = np.sum(y*inv_pabs_yy)/T + if EGx[6] < nf7['min_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['min_EGx'] ) * (EGx[6] - nf7['min_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['min_EGx']) + np.abs(NE_Bound[6]) + else: + if EGx[6] > nf7['max_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['max_EGx'] ) * (EGx[6] - nf7['max_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['max_EGx']) + np.abs(NE_Bound[6]) + else: + NE_Bound[6] = simplified_ppval(nf7['pp'], EGx[6] ) + + # select the tightest upper bound + max_NE, max_i = np.max(NE_Bound), np.argmax(NE_Bound) + negentropy_array[n] = max_NE + Cost[iter] = np.copy(Cost[iter] - max_NE) + + # Perform orthogonal ICA + if max_i == 0: + # G1(x) = x^4 + if fastica_on : + grad = X.dot( (4* y* yy).T )/T + Edgx = 12 + else : + grad = X.dot((4 * weight * y * yy ).T ) / np.sum(weight) + vEGx = 2 * (EGx[0] > nf1['critical_point']) -1 + elif max_i == 2: + # G3(x) = np.abs(x)/ (1 + np.abs(x)) + if fastica_on : + grad = X.dot( (sign_y * inv_pabs_y * inv_pabs_y).T )/T + Edgx = np.sum(-2 * inv_pabs_y * inv_pabs_y * inv_pabs_y)/T + else : + grad = X.dot((weight * sign_y * inv_pabs_y * inv_pabs_y).T ) / np.sum(weight) + vEGx = 2 * (EGx[2] > nf3['critical_point']) -1 + elif max_i == 4: + # G5(x) = x* np.abs(x) /(10 + np.abs(x)) + if fastica_on : + grad = X.dot((abs_y *(20 + abs_y) * inv_p10abs_y * inv_p10abs_y).T )/T + Edgx = np.sum(200 * sign_y * inv_p10abs_y * inv_p10abs_y * inv_p10abs_y)/T + else : + grad = X.dot((weight * abs_y * (20 + abs_y) * inv_p10abs_y**2 ).T ) / np.sum(weight) + vEGx = 2 * (EGx[4] > nf5['critical_point']) -1 + elif max_i == 6: + # G7(x) = x / (1 + x**2) + if fastica_on : + grad = X.dot(((1 - yy)* inv_pabs_yy**2).T )/T + Edgx = np.sum(2 * y * (yy-3)* inv_pabs_yy* inv_pabs_yy* inv_pabs_yy)/T + else : + grad = X.dot((weight * (1 - yy) * inv_pabs_yy**2 ).T ) / np.sum(weight) + vEGx = 2 * (EGx[6] > nf7['critical_point']) -1 + if fastica_on : + w1 = grad - Edgx * w + else : + grad = vEGx * grad + w = np.reshape(w, (-1, 1)) + grad = grad - ((w.T).dot(grad)) * w + grad = grad / np.linalg.norm(grad) + w1 = w + mu * grad + + W[n, :] = np.copy(w1.T) + + W = np.copy(symdecor(W) ) + + if Cost[iter] < min_cost: + cost_increaser_counter = 0 + min_cost = np.copy(Cost[iter]) + best_W = np.copy(last_W) + max_negentropy = np.copy(negentropy_array) + else: + cost_increaser_counter = cost_increaser_counter + 1 + + min_cost_queue[iter] = np.copy(min_cost) + + + if fastica_on : + if cost_increaser_counter >= max_cost_increase_number or 1- np.min(np.abs(np.diag(W.dot(last_W.T)))) < tolerance: + cost_increaser_counter = 0 + W = np.copy(best_W ) + last_W = np.copy(W) + fastica_on = False + continue + else : + if cost_increaser_counter > stochastic_search_factor * max_cost_increase_number: + break + last_W = np.copy(W) + + W = np.copy(best_W) +############################################################################################################## +# Part 4: non-orthogonal ICA +# fix small step size for refinement, gradient search +############################################################################################################## + if verbose: + print('Non-orthogonal ICA stage.') + + # resort W based on correlation with reference signals + for kl in range(num_guess): + r_n_c = guess_mat[:, kl] + for lp in range(num_W): + w = W[lp, :].T + corr_w_guess[kl, lp] = epsilon( X.T.dot(w), r_n_c) + + + # may need auction to to choose order + max_index = np.argmax(np.abs(corr_w_guess), axis = 1) + + if len(np.unique(max_index)) != num_guess: + colsol, _ = auction((1- np.abs(corr_w_guess)).T) + max_index = colsol.T + + c = np.arange(0, num_W) + c = np.setdiff1d(c, max_index) + sort_order = np.concatenate((max_index, c)) + W = np.copy(W[sort_order, :]) + + last_W = np.copy(W) + best_W = np.copy(W) + Cost = np.zeros((max_iter_nonorth, 1)) + min_cost_queue = np.copy(min_cost * np.ones((max_iter_nonorth, 1))) + mu = 1 + min_mu = 1/200 + max_cost_increase_number = 3 + cost_increaser_counter = 0 + mu_idx = np.full(mu_c.shape, False) + + decaying_factor = 0.95 + min_change = 1e-4 + min_iter = 100 + mu_old = np.copy(mu_c) + rho_n_arr = np.zeros((max_iter_nonorth, N)) + mu_signed = np.zeros((N, 1 )) + for iter in range(max_iter_nonorth): + Cost[iter] = np.copy(- np.log(np.abs(np.linalg.det(W)))) + + for n in range(N): + if N > 7: + if n == 0: + Wn = np.copy(W[1:N, :]) + inv_Q = np.copy(np.linalg.inv(Wn.dot(Wn.T))) + else: + n_last = np.copy(n-1) + Wn_last = np.copy(np.delete(W, n_last, axis = 0)) + w_current = np.copy(W[n, :].T ) + w_last = np.copy(W[n_last, :].T) + + c = Wn_last.dot(w_last- w_current) + c[n_last ] = 0.5* ((w_last.T).dot(w_last) - (w_current.T).dot(w_current) ) + e_last = np.zeros((N-1, 1)) + e_last[n_last] = 1 + + temp1 = np.reshape(inv_Q.dot(c), (-1, 1 )) + temp2 = np.reshape(inv_Q[:, n_last ], (-1, 1)) + inv_Q_plus = inv_Q - (temp1.dot(temp2.T) / (1 + temp1[n_last])) + + temp1 = np.reshape(inv_Q_plus.T.dot(c), (-1, 1)) + temp2 = np.reshape(inv_Q_plus[:, n_last ], (-1, 1 )) + inv_Q = inv_Q_plus - (temp1.dot(temp2.T) / (1 + c.T.dot(temp2))) + # make inv_Q hermitian + inv_Q = np.copy((inv_Q + inv_Q.T )/2 ) + + + + temp1 = np.random.rand(N, 1) + W_n = np.copy(np.delete(W, n, axis = 0)) + h = temp1 - W_n.T.dot(inv_Q.dot(W_n.dot(temp1))) + + else: + temp1 = np.random.rand(N, 1) + temp2 = np.copy(np.delete(W, n, axis = 0) ) + h = temp1 - temp2.T.dot(np.linalg.inv(temp2.dot(temp2.T)).dot(temp2.dot(temp1))) + + w = np.copy(W[n, :].T ) + y = np.copy(w.T.dot(X)) + + # evaluate the upper bound of negentropy of the nth component + NE_Bound = np.zeros((K, 1)) + EGx = np.zeros((K, 1)) + + # we only need to calculate these quantities once + yy = y* y + sign_y = np.sign(y) + abs_y = np.abs(y) + inv_pabs_y = 1/(1 + abs_y) + inv_pabs_yy = 1/(1+ yy) + inv_p10abs_y = 1/(10+abs_y) + + # G1(x) = x^4 + EGx[0] = np.sum(yy*yy)/T + if EGx[0] < nf1['min_EGx']: + NE_Bound[0] = simplified_ppval(nf1['pp_slope'], nf1['min_EGx'] ) * (EGx[0] - nf1['min_EGx']) + NE_Bound[0] = simplified_ppval(nf1['pp'],nf1['min_EGx']) + np.abs(NE_Bound[0] ) + else: + if EGx[0] > nf1['max_EGx']: + NE_Bound[0] = 0 + else: + NE_Bound[0] = simplified_ppval(nf1['pp'], EGx[0] ) + + # G3(x) = np.abs(x)/ (1 + np.abs(x)) + EGx[2] = 1 - np.sum(inv_pabs_y)/T + + if EGx[2] < nf3['min_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['min_EGx'] ) * (EGx[2] - nf3['min_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['min_EGx']) + np.abs(NE_Bound[2]) + else: + if EGx[2] > nf3['max_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['max_EGx'] ) * (EGx[2] - nf3['max_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['max_EGx']) + np.abs(NE_Bound[2]) + + else: + NE_Bound[2] = simplified_ppval(nf3['pp'], EGx[2] ) + + + # G5(x) = x* np.abs(x) /(10 + np.abs(x)) + EGx[4] = np.sum( y * abs_y * inv_p10abs_y )/T + if EGx[4] < nf5['min_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['min_EGx'] ) * (EGx[4] - nf5['min_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['min_EGx']) + np.abs(NE_Bound[4]) + else: + if EGx[4] > nf5['max_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['max_EGx'] ) * (EGx[4] - nf5['max_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['max_EGx']) + np.abs(NE_Bound[4]) + else: + NE_Bound[4] = simplified_ppval(nf5['pp'], EGx[4] ) + + # G7(x) = x / (1 + x**2) + EGx[6] = np.sum(y*inv_pabs_yy)/T + if EGx[6] < nf7['min_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['min_EGx'] ) * (EGx[6] - nf7['min_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['min_EGx']) + np.abs(NE_Bound[6]) + else: + if EGx[6] > nf7['max_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['max_EGx'] ) * (EGx[6] - nf7['max_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['max_EGx']) + np.abs(NE_Bound[6]) + else: + NE_Bound[6] = simplified_ppval(nf7['pp'], EGx[6] ) + + # select the tightest upper bound + max_NE, max_i = np.max(NE_Bound), np.argmax(NE_Bound) + Cost[iter] = np.copy(Cost[iter] - max_NE ) + + # Include constraint here: + + if n < num_guess: + # choose reference signal + r_n_c = guess_mat[:, n] + # compute correlation + if constraint == 'psd' : + e_pair = epsilon_dot(y.T, r_n_c ) + else : + e_pair = epsilon(y.T, r_n_c ) + dis_wr = np.abs(e_pair) + + if mu_idx[n] == 1: + rho_n = np.max(rho[rho <= dis_wr]) + else: + rho_n = np.min(rho[rho > dis_wr]) + + if rho.size == 0: + rho_n = 0.01 + # store rho + rho_n_arr[iter, n] = np.copy(rho_n) + # update mu + mu_old[n] = np.copy(mu_c[n]) + + mu_idx[n] = mu_idx[n] or (mu_c[n] >= 1) + mu_idx[n] = mu_idx[n] and (mu_c[n] > 0) + mu_c[n] = np.minimum(1, mu_c[n]) + mu_c[n] = np.maximum(0, mu_c[n] + gam * (rho_n - dis_wr)) + + if constraint == 'psd' : + r_n_c = r_n_c - np.mean(r_n_c) + + r_n_c = r_n_c / np.linalg.norm(r_n_c) + + + if max_i == 0: + # G1(x) = x^4 + vEGx = 2 * (EGx[0] > nf1['critical_point']) - 1 + grad = X.dot((4 * y * yy).T) / T + EGx[0] = np.maximum(np.minimum(EGx[0], nf1['max_EGx']), nf1['min_EGx']) + grad = (h / (h.T.dot(w))) + np.reshape(X.dot((4 * y * yy).T) * simplified_ppval(nf1['pp_slope'], EGx[0]) / T, (-1, 1)) + elif max_i == 2: + # G3(x) = np.abs(x)/ (1 + np.abs(x)) + vEGx = 2 * (EGx[2] > nf3['critical_point']) - 1 + grad = X.dot((sign_y * inv_pabs_y * inv_pabs_y).T) / T + EGx[2] = np.maximum(np.minimum(EGx[2], nf3['max_EGx']), nf3['min_EGx']) + grad = (h / (h.T.dot(w))) + np.reshape(X.dot((sign_y * inv_pabs_y * inv_pabs_y).T) * simplified_ppval(nf3['pp_slope'], EGx[2]) / T, (-1, 1)) + elif max_i == 4: + # G5(x) = x* np.abs(x) /(10 + np.abs(x)) + vEGx = 2 * (EGx[4] > nf5['critical_point']) - 1 + grad = X.dot((abs_y * (20 + abs_y) * inv_p10abs_y * inv_p10abs_y).T) / T + EGx[4] = np.maximum(np.minimum(EGx[4], nf5['max_EGx']), nf5['min_EGx']) + grad = (h / (h.T.dot(w))) + np.reshape(X.dot((abs_y * (20 + abs_y) * inv_p10abs_y * inv_p10abs_y).T) * simplified_ppval(nf5['pp_slope'], EGx[4]) / T, (-1, 1)) + elif max_i == 6: + # G7(x) = x / (1 + x**2) + vEGx = 2 * (EGx[6] > nf7['critical_point']) - 1 + grad = X.dot(((1 - yy) * inv_pabs_yy ** 2).T) / T + EGx[6] = np.maximum(np.minimum(EGx[6], nf7['max_EGx']), nf7['min_EGx']) + grad = (h / (h.T.dot(w))) + np.reshape(X.dot(((1 - yy) * inv_pabs_yy ** 2).T) * simplified_ppval(nf7['pp_slope'], EGx[6]) / T, (-1, 1)) + + w = np.reshape(w, (-1, 1 )) + + # adapt gradient to include constraints + if n < num_guess: + grad = grad + epsilon_grad() + + grad = grad - ((w.T).dot(grad)) * w + grad = grad / np.linalg.norm(grad) + w1 = w + mu * grad + w1 = w1 / np.linalg.norm(w1) + W[n, :] = np.copy(w1.T ) + + + Cost[iter] = Cost[iter]- (np.sum(np.power(mu_c, 2 ) - np.power(mu_old, 2)) / (2* gam )) + mu = np.copy(np.maximum((decaying_factor**(iter + 1)) , min_mu)) + currentChange = np.maximum(0, np.max(1- np.abs(np.diag(last_W.dot(W.T))))) + + if currentChange < min_change and iter > min_iter: + best_W = np.copy(W) + break + else: + last_W = np.copy(W) + + W = best_W + W = W.dot(P) + + return W + + +############################################################################################################### +# These functions are used in the arc-EBM algorithm. +############################################################################################################### + + +def simplified_ppval(pp: dict, xs: float) -> float: + """Helper function for ICA EBM: simplified version of ppval. + This function evaluates a piecewise polynomial at a specific point. + + Args: + pp (dict): a dictionary containing the piecewise polynomial representation of a function + xs (float): the evaluation point + + Returns: + v (float): the value of the function at xs + """ + b = pp['breaks'][0] + c = pp['coefs'] + l_pieces = int(pp['pieces'] ) + k = 4 + # find index + index = float('nan ') + middle_index = float('nan ') + if xs > b[l_pieces-1]: + index = l_pieces-1 + else: + if xs < b[1]: + index = 0 + else : + low_index = 0 + high_index = l_pieces-1 + + while True : + middle_index = int(np.ceil(((0.6* low_index + 0.4* high_index)))) + if xs < b[middle_index]: + high_index = middle_index + else: + low_index = middle_index + if low_index == high_index -1: + index = low_index + break + # now go to local coordinates + xs = xs - b[index] + # nested multiplication + v = c[index, 0] + for i in range(1, k ): + v = v*xs + c[index, i] + return v + +def inv_sqrtmH(B: np.ndarray) -> np.ndarray: + """Helper function for ICA EBM: computes the inverse square root of a matrix. + + Args: + B (np.ndarray): a square matrix + + Returns: + A (np.ndarray): the inverse square root of B + """ + + D, V = np.linalg.eig(B) + order = np.argsort(D) + D = D[order] + V = V[:, order] + d = 1/np.sqrt(D) + A = np.dot(np.dot(V, np.diag(d)), V.T) + return A + +def pre_processing(X: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """Helper function for ICA EBM: pre-processing (DC removal & spatial pre-whitening). + + Args: + X (np.ndarray, (Channels, Time Points) ): the data matrix [N x T] + + Returns: + X (np.ndarray, (Channels, Time Points)): the pre-processed data matrix [N x T] + P (np.ndarray, (Channels, Channels)): the pre-whitening matrix [N x N] + """ + + # pre-processing program + T = X.shape[1] + # remove DC + Xmean = np.mean(X, axis=1) + X = X - np.tile(Xmean, (T, 1)).T + # spatio pre-whitening + R = np.dot(X, X.T) / T + P = inv_sqrtmH(R) + X = np.dot(P, X) + return X, P + +def symdecor(M: np.ndarray) -> np.ndarray: + """Helper function for ICA EBM: fast symmetric orthogonalization. + + Args: + M (np.ndarray, (Channels, Channels)): the matrix to be orthogonalized [N x N] + + Returns: + W (np.ndarray, (Channels, Channels)): the orthogonalized matrix [N x N] + """ + + D, V = np.linalg.eig(M.dot(M.T)) + order = np.argsort(D) + D = D[order] + V = V[:, order] + B = np.dot(np.ones((M.shape[1], 1)), np.reshape((1/np.sqrt(D)).T, (1, M.shape[1]) )) + W = np.multiply(V, B ).dot(V.T.dot(M)) + return W + + +def auction(assignCost, guard=None): + """ + Performs assignment using Bertsekas' auction algorithm. + + Parameters: + assignCost (ndarray): m x n matrix of costs for associating each row with each column. m >= n. + guard (float, optional): Cost of column non-assignment. All assignments will have cost < guard. + + Returns: + colsol (ndarray): Column assignments, where colsol[j] gives the row assigned to column j. + rowsol (ndarray): Row assignments, where rowsol[i] gives the column assigned to row i. + """ + + m, n = assignCost.shape + + if m < n: + raise ValueError('Cost matrix must have no more columns than rows.') + + # Augment cost matrix with a guard row if specified. + m0 = m + if guard is not None and np.isfinite(guard): + m += 1 + assignCost = np.vstack((assignCost, np.full((1, n), guard))) + + # Initialize return arrays + colsol = np.zeros(n, dtype=int) + rowsol = np.zeros(m, dtype=int) + price = np.zeros(m) + EPS = np.sqrt(np.finfo(float).eps) / (n + 1) + + # 1st step is a full parallel solution. Get bids for all columns + jp = np.arange(n) + f = assignCost.copy() + b1 = np.min(f, axis=0) # cost of the best choice for each column + ip = np.argmin(f, axis=0) # row index of the best choice for each column + f[ip, jp] = np.inf # eliminate the best from contention + + bids = np.min(f, axis=0) - b1 # cost of runner-up choice hence bid + ibid = np.argsort(bids) # Arrange bids so highest are last + + # Now assign best bids (lesser bids are overwritten by better ones). + price[ip[ibid]] += EPS + bids[ibid] + rowsol[ip[ibid]] = jp[ibid] + 1 # +1 to convert to 1-based indexing + iy = np.nonzero(rowsol)[0] + colsol[rowsol[iy] - 1] = iy + 1 # -1 to convert back to 0-based indexing for Python + + # The guard row cannot be assigned (always available) + if m0 < m: + price[m - 1] = 0 + rowsol[m - 1] = 0 + + # Now Continue with non-parallel code handling any contentions. + while not np.all(colsol): + for jp in np.where(colsol == 0)[0]: + f = assignCost[:, jp] + price # costs + b1 = np.min(f) # cost and row of the best choice + ip = np.argmin(f) + if ip >= m0: + colsol[jp] = m + else: + f[ip] = np.inf # eliminate from contention + price[ip] += EPS + np.min(f) - b1 # runner-up choice hence bid + if rowsol[ip]: # take the row away if already assigned + colsol[rowsol[ip] - 1] = 0 + rowsol[ip] = jp + 1 # +1 to convert to 1-based indexing + colsol[jp] = ip + 1 # +1 to convert to 1-based indexing + + # Screen out infeasible assignments + if m > m0: + colsol[colsol == m] = 0 + rowsol = rowsol[:m0] + + return colsol - 1, rowsol - 1 # -1 to convert back to 0-based indexing for Python + diff --git a/src/cedalion/sigdecomp/multimodal/arc_erbm.py b/src/cedalion/sigdecomp/multimodal/arc_erbm.py new file mode 100644 index 00000000..17dfd342 --- /dev/null +++ b/src/cedalion/sigdecomp/multimodal/arc_erbm.py @@ -0,0 +1,919 @@ +"""Independent Component Analysis by Entropy Bound Rate Minimization (ICA-ERBM). + +This code is based on :cite:t:`Li2010B` and :cite:t:`Fu2014`. It was converted from +matlab versions provided by the MLSP Lab at the University of Maryland, which is +available here: https://mlsp.umbc.edu/resources.html. +""" + + +import scipy as sp +import numpy as np +from cedalion.sigdecomp.multimodal import arc_ebm as arc_ebm +import cedalion.data + +def arc_erbm(X: np.ndarray, guess_mat, p: int = None , pr_guess_mat = None) -> np.ndarray: + """ Adaptive-reverse Constrained ICA by Entropy Rate Bound Minimization (arc-ERBM) is a spectrally constrained ICA algorithm. + + Args: + X (np.ndarray, (Channels, Time Points)): the [N x T] input multivariate time series with dimensionality N observations/channels and T time points + + guess_mat (np.ndarray, (Time Points/2 , Referenced Channels)): Frequency reference signal for the reconstruction + + p (int): the filter length for the invertible filter source model, does not need to be specified. Default is p = minimum(11, T/50). + + pr_guess_mat (np.ndarray, (Time Points, Referenced Channels)): Optional time domain reference signal for the reconstruction, however, only frequency characteristics are used. Only needed if Phase Retrieval Projection constraint should be included. + + Returns: + W (np.ndarray, (Channels, Channels)): the [N x N] demixing matrix with weights for N channels/sources. To obtain the independent components, + the demixed signals can be calculated as S = W @ X. + + Initial Contributors: + - Jacqueline Behrendt | j.behrendt@tu-berlin.de | 2026 + + References: + This code is based on the matlab version of bss by Xi-Lin Li (:cite:t:`Li2010B`) + Xi-Lin Li, Tulay Adali, "Blind spatiotemporal separation of second and/or + higher-order correlated sources by entropy rate minimization," + IEEE International Conference on Acoustics, Speech and Signal Processing 2010. + The original matlab version is available at https://mlsp.umbc.edu/resources.html + under the name "Real-valued ICA by entropy bound minimization (ICA-EBM)" + """ + +################# Part 0: pre-processing ################# + + # load measuring functions as global variables + global nf1, nf3, nf5, nf7 + + file_path = cedalion.data.get("measfunc_table.npy") + table = np.load(file_path, allow_pickle=True) + + K = 8 + nf1, nf3, nf5, nf7 = table[0], table[2], table[4], table[6] + + # Apply pre-processing to data + N, T = X.shape + X, P = pre_processing(X) + + # initialize p if it is not provided + if p is None: + p = int(np.minimum(11, T/ 50)) + + if pr_guess_mat is None: + constraint = 'psd' + else: + constraint = 'phase_retrieval' + + # Define similarity measures and gradients for constraints + # normalize reference signals + for i in range(guess_mat.shape[1]): + r_n_c = guess_mat[:, i] + r_n_c = r_n_c - np.mean(r_n_c) + guess_mat[:, i] = np.copy(r_n_c / np.linalg.norm(r_n_c) ) + + # compute psd of X + X_hat = (2/T) * np.fft.rfft(X, axis = 1) + + # compute cross psd of X + C_hat = np.zeros((X_hat.shape[1], N , N ), dtype=complex) + C_hat = (X_hat[:, None, :] * np.conjugate(X_hat[None, :, :])).transpose(2, 0, 1) + + # center C_hat + C_hat_mean = np.mean(C_hat, axis = 0) + C_hat = C_hat - np.reshape(C_hat_mean, (1, N, N)) + + # store real matrix for gradient computation + C_tilde = np.real(C_hat + np.transpose(C_hat, (0, 2, 1)) ) + + # define similarity measure for psd constraint + def epsilon(a,b): + # power spectral density + # b is already a psd + psd_a = (2/T) * np.abs(np.fft.rfft(a))**2 + psd_correlation = np.corrcoef(psd_a, b)[0,1] + return psd_correlation + + def epsilon_grad(r_n_c): + # compute gradient of epsilon for the psd constraint + # compute psd of estimated source + psd_s = (2/T) * np.abs(np.fft.rfft(w.T.dot(X)))**2 + + # compute correlation between estimated and reference psd + current_corr = np.corrcoef(psd_s, r_n_c)[0,1] + sign = np.sign(current_corr) + + # compute gradient vector + vec = (sign / np.linalg.norm(psd_s, 2)) * r_n_c - (np.abs(current_corr) / np.linalg.norm(psd_s, 2)**2) * psd_s + vec = vec.reshape((-1, 1, 1)) + c_grad = (T/2) * mu_c[n] * np.sum( np.multiply(np.dot(C_tilde, w), vec), axis = 0 ) + return c_grad + + if constraint == 'phase_retrieval' : + amplitude = pr_guess_mat + + def pr_update(amp, y, filter, X_filtered): + # this function applies a phase retrieval update step + + # filter reference amplitude + amp_filtered = sp.signal.lfilter(filter, 1, amp , axis = 0 ) + amp_filtered = np.abs(np.fft.rfft(amp_filtered)) + + # compute fft of estimated source + y_hat = np.fft.rfft(y) + + # project onto magnitude constraint + g_hat = amp_filtered * np.exp(1j * np.angle(y_hat)) + g_hat = g_hat.reshape((-1, 1)) + + # inverse fft to time domain + g = np.fft.irfft(g_hat, axis = 0 , n =X_filtered.shape[1] ) + + # compute corresponding weights + returns = np.linalg.lstsq(X_filtered.T, g.flatten()) + v_tilde = returns[0] + + return v_tilde + + + # initialize W + W = arc_ebm.arc_ebm(X, guess_mat, 'psd') + + gam = 2500 + decaying_factor = 0.99 + + # Choose set of threshold values + rho = np.arange(0, 1.01, 0.01) + + + # Number of reference signals + num_guess = guess_mat.shape[1] + + mu_c = np.ones((num_guess, 1)) + corr_w_guess = np.zeros((num_guess, N)) + num_W = np.shape(W)[0] + corr_w_guess = np.zeros((num_guess, num_W)) + + # Resort W based on correlation with reference signals + for kl in range(num_guess): + r_n_c = guess_mat[:, kl] + for lp in range(num_W): + w = W[lp, :].T + corr_w_guess[kl, lp] = epsilon(X.T.dot(w), r_n_c) + + # May need auction to auction to choose order + max_index = np.argmax(np.abs(corr_w_guess), axis = 1) + if len(np.unique(max_index)) != num_guess: + colsol, _ = auction((1- np.abs(corr_w_guess)).T) + max_index = colsol.T + c = np.arange(0, num_W) + c = np.setdiff1d(c, max_index) + sort_order = np.concatenate((max_index, c)) + W = W[sort_order, :] + + + if p == 1: + W = W.dot(P) + return W + + # prediction coefficients + a = np.zeros((p, N )) + for n in range(N): + a[int(np.floor((p+1)/2) - 1 ), n ] = 1 + + Rz = np.zeros((N, N, N)) + temp5 = np.zeros((T, N)) + Z = np.zeros((N,T,N)) + + # Prepare the data used in integral + calculate_cos_sin_mtx(p) + + + last_W = np.copy(W) + best_W = np.copy(W) + best_a = np.copy(a) + + ################# Part 1: ################# + for stochastic_search in range(1,-1, -1): + if stochastic_search ==1 : + mu = 1/5 + max_cost_increase = 5 + max_iter_north = 500 + tolerance = 1e-3 + else: + mu = 1/50 + max_cost_increase = 3 + max_iter_north = 200 + tolerance = 1e-5 + + cost_increase_counter = 0 + mu_idx = np.full(mu_c.shape, False) + + min_mu = 1/200 + mu_old = np.copy(mu_c) + rho_n_arr = np.zeros((max_iter_north+1, N)) + + W = np.copy(best_W) + a = np.copy(best_a) + last_W = np.copy(best_W) + Cost = np.zeros((max_iter_north+1, 1)) + min_cost = np.inf + min_cost_queue = min_cost * np.ones((max_iter_north+1, 1)) + negentropy_array = np.zeros((N,1)) + + for iter in range(1, max_iter_north+1): + + if stochastic_search == 1: + # estimate AR coefficients + Y = np.copy(np.dot(W, X) ) + for n in range(N): + + if iter%6 == 1 or iter<= 5: + + a1, min_ere1 = lfc(Y[n,:], p , 'unknown', []) + a2, min_ere2 = lfc(Y[n, :], p, [], a[:, n]) + + # choose the best model + min_ere = np.inf + if min_ere > min_ere1: + min_ere = min_ere1 + a[:, n] = np.copy(a1) + if min_ere > min_ere2: + min_ere = min_ere2 + a[:, n] = np.copy(a2) + + elif iter%6 == 4 : + a3, _ = lfc(Y[n, :], p, [], a[:, n]) + a[:, n ] = np.copy(a3) + + temp5 = sp.signal.lfilter(a[:, n].T, 1, X.T , axis = 0 ) + Rz[ :, :, n] = np.dot(temp5.T, temp5) / T + Z[:, :, n] = np.copy(temp5.T) + + Cost[iter-1] = np.copy(- np.log(np.abs(np.linalg.det(W)))) + + # estimate W + for n in range(N): + temp1 = np.random.rand(N, 1) + temp2 = np.delete(W, n, axis = 0) + h = temp1 - temp2.T.dot( np.linalg.solve( np.dot(temp2, temp2.T), temp2)).dot(temp1 ) + v = np.copy(W[n, :].T ) + sigma2 = v.T.dot(Rz[:, :, n]).dot(v) + Cost[iter-1] = np.copy(Cost[iter-1] + np.log(sigma2)/2 ) + v = np.copy(v / np.sqrt(sigma2)) + + # prediction error + y = np.copy(v.T.dot(Z[:, :, n ])) + + # evaluate the upper bound of negentropy of the n-th component + NE_Bound = np.zeros((K, 1)) + EGx = np.zeros((K, 1)) + # we only need to calculate these quantities once + yy = y* y + sign_y = np.sign(y) + abs_y = np.abs(y) + inv_pabs_y = 1/(1 + abs_y) + inv_pabs_yy = 1/(1+ yy) + inv_p10abs_y = 1/(10+abs_y) + + # G1(x) = x^4 + EGx[0] = np.sum(yy*yy)/T + if EGx[0] < nf1['min_EGx']: + NE_Bound[0] = simplified_ppval(nf1['pp_slope'], nf1['min_EGx'] ) * (EGx[0] - nf1['min_EGx']) + NE_Bound[0] = simplified_ppval(nf1['pp'],nf1['min_EGx']) + np.abs(NE_Bound[0] ) + else: + if EGx[0] > nf1['max_EGx']: + NE_Bound[0] = 0 + else: + NE_Bound[0] = simplified_ppval(nf1['pp'], EGx[0] ) + + # G3(x) = np.abs(x)/ (1 + np.abs(x)) + EGx[2] = 1 - np.sum(inv_pabs_y)/T + if EGx[2] < nf3['min_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['min_EGx'] ) * (EGx[2] - nf3['min_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['min_EGx']) + np.abs(NE_Bound[2]) + else: + if EGx[2] > nf3['max_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['max_EGx'] ) * (EGx[2] - nf3['max_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['max_EGx']) + np.abs(NE_Bound[2]) + + else: + NE_Bound[2] = simplified_ppval(nf3['pp'], EGx[2] ) + + # G5(x) = x* np.abs(x) /(10 + np.abs(x)) + EGx[4] = np.sum( y * abs_y * inv_p10abs_y )/T + if EGx[4] < nf5['min_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['min_EGx'] ) * (EGx[4] - nf5['min_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['min_EGx']) + np.abs(NE_Bound[4]) + else: + if EGx[4] > nf5['max_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['max_EGx'] ) * (EGx[4] - nf5['max_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['max_EGx']) + np.abs(NE_Bound[4]) + else: + NE_Bound[4] = simplified_ppval(nf5['pp'], EGx[4] ) + + # G7(x) = x / (1 + x**2) + EGx[6] = np.sum(y*inv_pabs_yy)/T + if EGx[6] < nf7['min_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['min_EGx'] ) * (EGx[6] - nf7['min_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['min_EGx']) + np.abs(NE_Bound[6]) + else: + if EGx[6] > nf7['max_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['max_EGx'] ) * (EGx[6] - nf7['max_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['max_EGx']) + np.abs(NE_Bound[6]) + else: + NE_Bound[6] = simplified_ppval(nf7['pp'], EGx[6] ) + + # select the tightest upper bound + max_NE, max_i = np.max(NE_Bound), np.argmax(NE_Bound) + negentropy_array[n] = np.copy(max_NE) + Cost[iter -1] = np.copy(Cost[iter-1] - max_NE) + + if n < num_guess: + # choose reference signal + r_n_c = guess_mat[:, n] + # compute correlation + w = np.copy(W[n, :].T ) + y_tilde = np.copy(w.T.dot(X)) + w = w.reshape((-1, 1)) + + e_pair = epsilon(y_tilde.T, r_n_c ) + # abs of similarity measure + dis_wr = np.abs(e_pair) + + # choose rho_n based on update scheme + if mu_idx[n] == 1: + if np.size(rho[rho < dis_wr]) != 0: + rho_n = np.max(rho[rho < dis_wr]) + else: + if np.size(rho[rho > dis_wr]) != 0: + rho_n = np.min(rho[rho > dis_wr]) + + if rho.size == 0: + rho_n = 0.01 + + # store rho + rho_n_arr[iter, n] = np.copy(e_pair) + # update mu + mu_old[n] = np.copy(mu_c[n]) + + mu_idx[n] = mu_idx[n] or (mu_c[n] >= 1) + mu_idx[n] = mu_idx[n] and (mu_c[n] > 0) + mu_c[n] = np.minimum(1, mu_c[n]) + mu_c[n] = np.maximum(0, mu_c[n] + gam * (rho_n - dis_wr)) + + + + if stochastic_search == 1: + weight = np.random.rand(1, T) + else: + weight = np.ones((1, T)) + + if max_i == 0: + EGx[0] = np.maximum(np.minimum(EGx[0], nf1['max_EGx']), nf1['min_EGx']) + grad = h / (np.dot(h.T, v)) + Z[:, :, n].dot((4* weight*y*yy).T) * simplified_ppval(nf1['pp_slope'], EGx[0]) / np.sum(weight) + if max_i == 2: + EGx[2] = np.maximum(np.minimum(EGx[2], nf3['max_EGx']), nf3['min_EGx']) + grad = h / (np.dot(h.T, v)) + Z[:, :, n].dot((weight* sign_y*inv_pabs_y**2).T) * simplified_ppval(nf3['pp_slope'], EGx[2]) / np.sum(weight) + if max_i == 4: + EGx[4] = np.maximum(np.minimum(EGx[4], nf5['max_EGx']), nf5['min_EGx']) + grad = h / (np.dot(h.T, v)) + Z[:, :, n].dot((weight* abs_y*(20+abs_y)*inv_p10abs_y**2).T) * simplified_ppval(nf5['pp_slope'], EGx[4]) / np.sum(weight) + if max_i == 6: + EGx[6] = np.maximum(np.minimum(EGx[6], nf7['max_EGx']), nf7['min_EGx']) + grad = h / (np.dot(h.T, v)) + Z[:, :, n].dot((weight*(1-yy)*inv_pabs_yy**2).T) * simplified_ppval(nf7['pp_slope'], EGx[6]) / np.sum(weight) + + # Constant direction + cnstd = Rz[:, :, n].dot(v) + + if n < num_guess: + constraint_grad = epsilon_grad(r_n_c) + grad = grad + constraint_grad + + + # projected gradient + grad = grad - (cnstd.T.dot(grad) * cnstd /(np.dot(cnstd.T, cnstd))).reshape(-1, 1) + check = inv_sqrtmH(Rz[:, :, n]) + grad = check.dot(grad) + + # Normalized gradient + grad = grad / np.sqrt(grad.T.dot(Rz[:, :, n].dot(grad))) + + v = v.reshape(-1,1) + mu * grad + + + if constraint == 'phase_retrieval' and stochastic_search == 1: + if n < amplitude.shape[1]: + v_tilde = pr_update(amplitude[:, n], y, a[:, n], Z[:, :, n]) + v_tilde = np.reshape(v_tilde, v.shape) + v = 0.8* v + 0.2* v_tilde + + W[n, :] = np.copy(v.T ) + + + Cost[iter] = Cost[iter] - (np.sum(np.power(mu_c, 2 ) - np.power(mu_old, 2)) / (2 * gam )) + + + if Cost[iter-1] < min_cost: + cost_increase_counter = 0 + min_cost = np.copy(Cost[iter-1]) + best_W = np.copy(last_W) + best_a = np.copy(a) + else: + cost_increase_counter = cost_increase_counter + 1 + + min_cost_queue[iter-1] = np.copy(min_cost) + + if cost_increase_counter > max_cost_increase: + if stochastic_search == 1: + W1 = np.copy(W) + last_W1 = np.copy(last_W) + for n in range(N): + W1[n, :] = W1[n, :] / np.linalg.norm(W1[n, :]) + last_W1[n, :] = last_W1[n, :] / np.linalg.norm(last_W1[n, :]) + if 1 - np.min(np.abs(np.diag(np.dot(W1, last_W1.T)))) < tolerance: + break + else: + mu = np.copy(np.maximum((decaying_factor**(iter + 1)) , min_mu)) + W = np.copy(best_W) + last_W = np.copy(best_W) + a = np.copy(best_a) + cost_increase_counter = 0 + continue + else: + W1 = np.copy(W) + last_W1 = np.copy(last_W) + for n in range(N): + W1[n, :] = W1[n, :] / np.linalg.norm(W1[n, :]) + last_W1[n, :] = last_W1[n, :] / np.linalg.norm(last_W1[n, :]) + if 1 - np.min(np.abs(np.diag(np.dot(W1, last_W1.T)))) < tolerance: + break + else: + mu = np.copy(np.maximum((decaying_factor**(iter + 1)) , min_mu)) + W = np.copy(best_W) + last_W = np.copy(best_W) + a = np.copy(best_a) + cost_increase_counter = 0 + continue + + last_W = np.copy(W) + + W = np.copy(best_W) + W = np.dot(W, P) + + return W + + +############################################################################################################### +# These functions are used in the ERBM algorithm. +############################################################################################################### + + +def lfc(x: np.ndarray, p: int , choice, a0) -> tuple[np.ndarray, np.ndarray]: + """Helper function for ERBM ICA: computes the linear filtering coefficients (LFC) with length p for entropy rate estimation, and the estimated entropy rate. + + Args: + x (np.ndarray, (Time Points, 1)): the source estimate [T x 1] + p (int): the filter length for the source model + choice : can be 'sub', 'super' or 'unknown'; any other input is handled as 'unknown' + a0 (np.ndarray or empty list): is the intial guess [p x 1] or an empty list [] + + Returns: + a (np.ndarray, (p, 1)): the filter coefficients [p x 1] + min_cost (np.ndarray, (1, 1)): the entropy rate estimation [1 x 1] + """ + + global nf1, nf3, nf5, nf7, cosmtx, sinmtx, Simpson_c + + tolerance = 1e-4 + T = x.shape[0] + X0 = sp.linalg.convolution_matrix(x, p, 'full').T + # remove tail so outliers have less effect + X = X0[:, : T ] + # remove DC + X = X - np.mean(X, axis = 1).reshape(-1, 1) + # pre-whitening + R = np.dot(X, X.T) / T + D, V = np.linalg.eig(R) + order = np.argsort(D) + d = D[order] + V = V[:, order] + eps = np.finfo(np.float64).eps + d[d < 10 * eps]= 10 * eps + P = np.dot(np.dot(V, np.diag(1/np.sqrt(d))), V.T) + X = np.dot(P, X) + + if np.size(a0) == 0: + # use SEA to provide the initial guess + if choice == 'sub': + # we don't need this case + # TO DO + pass + if choice == 'super': + # TO DO + pass + else: + a = np.random.rand(p,1) + a = a / np.linalg.norm(a) + last_a = np.copy(a) + for iter in range(100): + y = np.dot(a.T, X) + a = X.dot((y**3).T) / T - 3 * a + a = np.copy(a / np.linalg.norm(a)) + if 1 - np.abs(a.T.dot(last_a)) < tolerance: + break + else: + last_a = np.copy(a) + + else: + a = np.linalg.solve(P, a0) + + min_cost = np.inf + K = 8 # number of measuring functions + best_a = np.copy(a) + last_a = np.copy(a) + min_mu = 1/128 + if np.size(a0) == 0: + max_iter = 100 + mu = 4* min_mu + else: + max_iter = 100 + mu = 16* min_mu + cost_increase_counter = 0 + Cost = np.zeros((max_iter, 1)) + + for iter in range(max_iter): + a = np.copy(np.reshape(a, (-1, 1)) ) + a_original = np.copy(P.dot(a)) + b_original, G_original = cnstd_and_gain(a_original) + + a = a.dot(np.exp(- G_original/2)) + b = P.dot(b_original) + y = np.copy(np.dot(a.T, X)) + sigma2 = np.dot(a.T, a) + # normalized y + y = np.copy(y / np.sqrt(sigma2)) + + Cost[iter] = np.copy(0.5 * np.log(2 * np.pi * sigma2) + 0.5) + + NE_Bound = np.zeros((K, 1)) + EGx = np.zeros((K, 1)) + # we only need to calculate these quantities once + yy = y* y + sign_y = np.sign(y) + abs_y = np.abs(y) + inv_pabs_y = 1/(1 + abs_y) + inv_pabs_yy = 1/(1+ yy) + inv_p10abs_y = 1/(10+abs_y) + + # G1(x) = x^4 + EGx[0] = np.sum(yy*yy)/T + if EGx[0] < nf1['min_EGx']: + NE_Bound[0] = simplified_ppval(nf1['pp_slope'], nf1['min_EGx'] ) * (EGx[0] - nf1['min_EGx']) + NE_Bound[0] = simplified_ppval(nf1['pp'],nf1['min_EGx']) + np.abs(NE_Bound[0] ) + else: + if EGx[0] > nf1['max_EGx']: + NE_Bound[0] = 0 + else: + NE_Bound[0] = simplified_ppval(nf1['pp'], EGx[0] ) + + # G3(x) = np.abs(x)/ (1 + np.abs(x)) + EGx[2] = 1 - np.sum(inv_pabs_y)/T + if EGx[2] < nf3['min_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['min_EGx'] ) * (EGx[2] - nf3['min_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['min_EGx']) + np.abs(NE_Bound[2]) + else: + if EGx[2] > nf3['max_EGx']: + NE_Bound[2] = simplified_ppval(nf3['pp_slope'], nf3['max_EGx'] ) * (EGx[2] - nf3['max_EGx']) + NE_Bound[2] = simplified_ppval(nf3['pp'], nf3['max_EGx']) + np.abs(NE_Bound[2]) + + else: + NE_Bound[2] = simplified_ppval(nf3['pp'], EGx[2] ) + + # G5(x) = x* np.abs(x) /(10 + np.abs(x)) + EGx[4] = np.sum( y * abs_y * inv_p10abs_y )/T + if EGx[4] < nf5['min_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['min_EGx'] ) * (EGx[4] - nf5['min_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['min_EGx']) + np.abs(NE_Bound[4]) + else: + if EGx[4] > nf5['max_EGx']: + NE_Bound[4] = simplified_ppval(nf5['pp_slope'], nf5['max_EGx'] ) * (EGx[4] - nf5['max_EGx']) + NE_Bound[4] = simplified_ppval(nf5['pp'], nf5['max_EGx']) + np.abs(NE_Bound[4]) + else: + NE_Bound[4] = simplified_ppval(nf5['pp'], EGx[4] ) + + # G7(x) = x / (1 + x**2) + EGx[6] = np.sum(y*inv_pabs_yy)/T + if EGx[6] < nf7['min_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['min_EGx'] ) * (EGx[6] - nf7['min_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['min_EGx']) + np.abs(NE_Bound[6]) + else: + if EGx[6] > nf7['max_EGx']: + NE_Bound[6] = simplified_ppval(nf7['pp_slope'], nf7['max_EGx'] ) * (EGx[6] - nf7['max_EGx']) + NE_Bound[6] = simplified_ppval(nf7['pp'], nf7['max_EGx']) + np.abs(NE_Bound[6]) + else: + NE_Bound[6] = simplified_ppval(nf7['pp'], EGx[6] ) + + # select the tightest upper bound + max_NE, max_i = np.max(NE_Bound), np.argmax(NE_Bound) + Cost[iter] = np.copy(Cost[iter] - max_NE) + last_a = np.copy(a) + + if Cost[iter] < min_cost: + cost_increase_counter = 0 + min_cost = np.copy(Cost[iter]) + best_a = np.copy(a) + else: + cost_increase_counter = cost_increase_counter + 1 + + if cost_increase_counter > 0: + if mu > min_mu: + mu = mu / 2 + cost_increase_counter = 0 + a = np.copy(best_a) + last_a = np.copy(best_a) + continue + else: + break + + grad = a / sigma2 + if max_i == 0: + EGx[0] = np.maximum(np.minimum(EGx[0], nf1['max_EGx']), nf1['min_EGx']) + grad = grad - X.dot((4*y * yy).T) * simplified_ppval(nf1['pp_slope'], EGx[0]) / T /np.sqrt(sigma2) + grad = grad + np.sum(4* y* yy* y ) * simplified_ppval(nf1['pp_slope'], EGx[0])* a / T / sigma2 + if max_i == 2: + EGx[2] = np.maximum(np.minimum(EGx[2], nf3['max_EGx']), nf3['min_EGx']) + grad = grad - X.dot( sign_y *inv_pabs_y**2) * simplified_ppval(nf3['pp_slope'], EGx[2]) / T / np.sqrt(sigma2) + grad = grad + np.sum(sign_y*inv_pabs_y**2*y) * simplified_ppval(nf3['pp_slope'], EGx[2]) * a / T / sigma2 + if max_i == 4: + EGx[4] = np.maximum(np.minimum(EGx[4], nf5['max_EGx']), nf5['min_EGx']) + grad = grad - X.dot( abs_y*(20+abs_y)*inv_p10abs_y**2) * simplified_ppval(nf5['pp_slope'], EGx[4]) / T / np.sqrt(sigma2) + grad = grad + np.sum( abs_y*(20+abs_y)*inv_p10abs_y**2*y ) * simplified_ppval(nf5['pp_slope'], EGx[4]) * a / T / sigma2 + if max_i == 6: + EGx[6] = np.maximum(np.minimum(EGx[6], nf7['max_EGx']), nf7['min_EGx']) + grad = grad - X.dot( (1-yy)*inv_pabs_yy**2) * simplified_ppval(nf7['pp_slope'], EGx[6]) / T / np.sqrt(sigma2) + grad = grad + np.sum( (1-yy)*inv_pabs_yy**2*y) * simplified_ppval(nf7['pp_slope'], EGx[6]) * a / T / sigma2 + + + grad = grad- np.reshape(np.dot(grad.T, b)*b/(np.dot(b.T, b)) , (1, -1)) + grad = np.sqrt(sigma2) * grad/ np.linalg.norm(grad) + a = np.copy(a - mu * grad) + + a = np.reshape(a ,(-1, 1)) + a = np.copy(best_a) + a = np.dot(P,a) + + return a, min_cost + + +def simplified_ppval(pp: dict, xs: float) -> float: + """Helper function for ERBM ICA: simplified version of ppval. + This function evaluates a piecewise polynomial at a specific point. + + Args: + pp (dict): a dictionary containing the piecewise polynomial representation of a function + xs (float): the evaluation point + + Returns: + v (float): the value of the function at xs + """ + + b = pp['breaks'][0] + c = pp['coefs'] + l_pieces = int(pp['pieces'] ) + k = 4 + # find index + index = float('nan ') + middle_index = float('nan ') + if xs > b[l_pieces-1]: + index = l_pieces-1 + else: + if xs < b[1]: + index = 0 + else : + low_index = 0 + high_index = l_pieces-1 + + while True : + middle_index = int(np.ceil(((0.6* low_index + 0.4* high_index)))) + if xs < b[middle_index]: + high_index = middle_index + else: + low_index = middle_index + if low_index == high_index -1: + index = low_index + break + # now go to local coordinates + xs = xs - b[index] + # nested multiplication + v = c[index, 0] + for i in range(1, k ): + v = v*xs + c[index, i] + return v + +def cnstd_and_gain(a: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """Helper function for ERBM ICA: returns constraint direction used for calculating projected gradient and gain of filter a. + + Args: + a (np.ndarray, (p, 1)): the filter coefficients [p x 1] + + Returns: + b (np.ndarray, (p, 1)): the constraint direction [p x 1] + G (np.ndarray, (1,)): the gain of the filter a + """ + + global cosmtx, sinmtx, Simpson_c + + + eps = np.finfo(np.float64).eps + p = a.shape[0] + # calculate the integral + # sample omega from 0 to pi + n = 10*p + h = np.pi / n + + # calculate |A(w)|^2 + Awr = np.zeros((1, n+1)) # real part + Awi = np.zeros((1, n+1)) # imaginary part + for q in range(p): + Awr = Awr + a[q] * cosmtx[q, :] + Awi = Awi + a[q] * sinmtx[q, :] + + Aw2 = 10*eps+ Awr**2 + Awi**2 + + # calculate the vector + v = np.zeros((p+1, n+1)) + inv_Aw2 = 1 / Aw2 + for q in range(p): + v[q, :] = cosmtx[q, :] * inv_Aw2 + v[p,:] = np.log(Aw2)/np.pi + + # this is the integral + u = h * v.dot(Simpson_c/3) + b = sp.linalg.toeplitz(u[:p].ravel()).dot(a) + + # gain + G = u[p] + return b, G + + + +def calculate_cos_sin_mtx(p: int) -> None : + """Helper function for ERBM ICA: calculates the cos and sin matrix for integral calculation in ERBM ICA. + + Args: + p (int): the filter length for the invertible filter source model + + Returns: + None + """ + + # prepare the cos and sin matrix for integral calculation + global cosmtx, sinmtx, Simpson_c + + # sample omega from 0 to pi + n = 10*p + h = np.pi / n + omega = np.arange(0, n+1, 1) * h + + cosmtx = np.zeros((p, n+1)) + sinmtx = np.zeros((p, n+1)) + for q in range(p): + cosmtx[q, :] = np.cos(q * omega) + sinmtx[q, :] = np.sin(q * omega) + # c ist the vetcor used in Simpson's rule + Simpson_c = np.zeros((n+1, 1)) + Simpson_c[np.arange(0, n+1, 2)] = 2 + Simpson_c[np.arange(1, n, 2)] = 4 + Simpson_c[0] = 1 + Simpson_c[n] = 1 + + +def pre_processing(X: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """Helper function for ERBM ICA: Preprocessing (removal of mean, patial pre-whitening, temporal pre-filtering) + + Args: + X (np.ndarray, (Channels, Time Points)): the [N x T] input multivariate time series with dimensionality N observations/channels and T time points + + Returns: + X (np.ndarray, (Channels, Time Points)): the pre-processed input multivariate time series + P (np.ndarray, (Channels, Channels)): the pre-whitening matrix + """ + # pre-processing of the data + N, T = X.shape + # remove mean + X = X - np.mean(X, axis = 1).reshape(N, 1) + # spatio pre-whitening + R = np.dot(X, X.T) / T + P1 = inv_sqrtmH(R) + X = np.dot(P1, X) + # temporal pre-filtering for colored signals + q = 3 + r = np.zeros((q, 1)) + for p in range(q): + r[p] = np.trace(X[:, : T-p].dot(X[:, p: T].T)) / T / N + + af = np.linalg.solve(sp.linalg.toeplitz(r[:q-1].ravel()), np.conjugate(r[1:]) ) + for n in range(N): + X[n, :] = sp.signal.lfilter(np.concatenate((np.ones((1,1)), -af), axis = 0)[:,0], 1 ,X[n, :]) + + # spatio pre-whitening + R = np.dot(X, X.T) / T + P2 = inv_sqrtmH(R) + X = np.dot(P2, X) + P = np.dot(P2, P1) + + return X, P + +def inv_sqrtmH(B: np.ndarray) -> np.ndarray: + """Helper function for ERBM ICA: computes the inverse square root of a matrix. + + Args: + B (np.ndarray): a square matrix + + Returns: + A (np.ndarray): the inverse square root of B + """ + D, V = np.linalg.eig(B) + order = np.argsort(D) + D = D[order] + V = V[:, order] + #print('D', D) + d = 1/np.sqrt(D) + A = np.dot(np.dot(V, np.diag(d)), V.T) + return A + +def auction(assignCost, guard=None): + """ + Performs assignment using Bertsekas' auction algorithm. + + Parameters: + assignCost (ndarray): m x n matrix of costs for associating each row with each column. m >= n. + guard (float, optional): Cost of column non-assignment. All assignments will have cost < guard. + + Returns: + colsol (ndarray): Column assignments, where colsol[j] gives the row assigned to column j. + rowsol (ndarray): Row assignments, where rowsol[i] gives the column assigned to row i. + """ + + m, n = assignCost.shape + + if m < n: + raise ValueError('Cost matrix must have no more columns than rows.') + + # Augment cost matrix with a guard row if specified. + m0 = m + if guard is not None and np.isfinite(guard): + m += 1 + assignCost = np.vstack((assignCost, np.full((1, n), guard))) + + # Initialize return arrays + colsol = np.zeros(n, dtype=int) + rowsol = np.zeros(m, dtype=int) + price = np.zeros(m) + EPS = np.sqrt(np.finfo(float).eps) / (n + 1) + + # 1st step is a full parallel solution. Get bids for all columns + jp = np.arange(n) + f = assignCost.copy() + b1 = np.min(f, axis=0) # cost of the best choice for each column + ip = np.argmin(f, axis=0) # row index of the best choice for each column + f[ip, jp] = np.inf # eliminate the best from contention + + bids = np.min(f, axis=0) - b1 # cost of runner-up choice hence bid + ibid = np.argsort(bids) # Arrange bids so highest are last + + # Now assign best bids (lesser bids are overwritten by better ones). + price[ip[ibid]] += EPS + bids[ibid] + rowsol[ip[ibid]] = jp[ibid] + 1 # +1 to convert to 1-based indexing + iy = np.nonzero(rowsol)[0] + colsol[rowsol[iy] - 1] = iy + 1 # -1 to convert back to 0-based indexing for Python + + # The guard row cannot be assigned (always available) + if m0 < m: + price[m - 1] = 0 + rowsol[m - 1] = 0 + + # Now Continue with non-parallel code handling any contentions. + while not np.all(colsol): + for jp in np.where(colsol == 0)[0]: + f = assignCost[:, jp] + price # costs + b1 = np.min(f) # cost and row of the best choice + ip = np.argmin(f) + if ip >= m0: + colsol[jp] = m + else: + f[ip] = np.inf # eliminate from contention + price[ip] += EPS + np.min(f) - b1 # runner-up choice hence bid + if rowsol[ip]: # take the row away if already assigned + colsol[rowsol[ip] - 1] = 0 + rowsol[ip] = jp + 1 # +1 to convert to 1-based indexing + colsol[jp] = ip + 1 # +1 to convert to 1-based indexing + + # Screen out infeasible assignments + if m > m0: + colsol[colsol == m] = 0 + rowsol = rowsol[:m0] + + return colsol - 1, rowsol - 1 # -1 to convert back to 0-based indexing for Python + + + + \ No newline at end of file diff --git a/src/cedalion/sigdecomp/unimodal/ica_ebm.py b/src/cedalion/sigdecomp/unimodal/ica_ebm.py index 65620c19..80bc19d5 100644 --- a/src/cedalion/sigdecomp/unimodal/ica_ebm.py +++ b/src/cedalion/sigdecomp/unimodal/ica_ebm.py @@ -52,21 +52,13 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: verbose = False # report the progress if verbose== True - # show the cost values vs. iterations at each stage if show_cost== True - # not implemented yet - show_cost = False - # Load 8 measuring functions. But we only use 4 of them. K = 8 - # table = np.load('measfunc_table.npy', allow_pickle= True) - file_path = cedalion.data.get("measfunc_table.npy") table = np.load(file_path, allow_pickle=True) - nf1, nf2, nf3, nf4, nf5, nf6, nf7, nf8 = ( - table[0], table[1], table[2], table[3], table[4], table[5], table[6], table[7] - ) + nf1, nf3, nf5, nf7 = table[0], table[2], table[4], table[6] N = X.shape[0] @@ -189,7 +181,6 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: min_mu = 1/50 cost_increaser_counter = 0 fastica_on = True - error = 0 max_negentropy = np.zeros((N, 1)) negentropy_array = np.zeros((N, 1)) for iter in range(max_iter_orth): @@ -268,7 +259,7 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: # Perform orthogonal ICA if max_i == 0: # G1(x) = x^4 - if fastica_on == True : + if fastica_on : grad = X.dot( (4* y* yy).T )/T Edgx = 12 else : @@ -276,7 +267,7 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: vEGx = 2 * (EGx[0] > nf1['critical_point']) -1 elif max_i == 2: # G3(x) = np.abs(x)/ (1 + np.abs(x)) - if fastica_on == True : + if fastica_on : grad = X.dot( (sign_y * inv_pabs_y * inv_pabs_y).T )/T Edgx = np.sum(-2 * inv_pabs_y * inv_pabs_y * inv_pabs_y)/T else : @@ -284,7 +275,7 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: vEGx = 2 * (EGx[2] > nf3['critical_point']) -1 elif max_i == 4: # G5(x) = x* np.abs(x) /(10 + np.abs(x)) - if fastica_on == True : + if fastica_on : grad = X.dot((abs_y *(20 + abs_y) * inv_p10abs_y * inv_p10abs_y).T )/T Edgx = np.sum(200 * sign_y * inv_p10abs_y * inv_p10abs_y * inv_p10abs_y)/T else : @@ -292,13 +283,13 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: vEGx = 2 * (EGx[4] > nf5['critical_point']) -1 elif max_i == 6: # G7(x) = x / (1 + x**2) - if fastica_on == True : + if fastica_on : grad = X.dot(((1 - yy)* inv_pabs_yy**2).T )/T Edgx = np.sum(2 * y * (yy-3)* inv_pabs_yy* inv_pabs_yy* inv_pabs_yy)/T else : grad = X.dot((weight * (1 - yy) * inv_pabs_yy**2 ).T ) / np.sum(weight) vEGx = 2 * (EGx[6] > nf7['critical_point']) -1 - if fastica_on == True : + if fastica_on : w1 = grad - Edgx * w else : grad = vEGx * grad @@ -321,12 +312,11 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: min_cost_queue[iter] = np.copy(min_cost) - if fastica_on == True : + if fastica_on : if cost_increaser_counter >= max_cost_increase_number or 1- np.min(np.abs(np.diag(W.dot(last_W.T)))) < tolerance: cost_increaser_counter = 0 W = np.copy(best_W ) last_W = np.copy(W) - iter_fastica = np.copy(iter) fastica_on = False continue else : @@ -348,7 +338,7 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: ############################################################################################################## # Part 2: check for saddle points ############################################################################################################## - if saddle_test_enable == True : + if saddle_test_enable : if verbose: print('Saddle point detection.') SADDLE_TESTED = False @@ -507,7 +497,7 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: SADDLE_TESTED = False - if SADDLE_TESTED == True : + if SADDLE_TESTED : ############################################################################################################## # Part 3: if saddle points are detected, refine orthogonal ICA # fix step size gradient search @@ -522,7 +512,6 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: mu = 1/ 50 cost_increaser_counter = 0 fastica_on = True - error = 0 for iter in range(max_iter_orth_refine): for n in range(N): @@ -595,7 +584,7 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: # Perform orthogonal ICA if max_i == 0: # G1(x) = x^4 - if fastica_on == True : + if fastica_on : grad = X.dot( (4* y* yy).T )/T Edgx = 12 else : @@ -603,7 +592,7 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: vEGx = 2 * (EGx[0] > nf1['critical_point']) -1 elif max_i == 2: # G3(x) = np.abs(x)/ (1 + np.abs(x)) - if fastica_on == True : + if fastica_on : grad = X.dot( (sign_y * inv_pabs_y * inv_pabs_y).T )/T Edgx = np.sum(-2 * inv_pabs_y * inv_pabs_y * inv_pabs_y)/T else : @@ -611,7 +600,7 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: vEGx = 2 * (EGx[2] > nf3['critical_point']) -1 elif max_i == 4: # G5(x) = x* np.abs(x) /(10 + np.abs(x)) - if fastica_on == True : + if fastica_on : grad = X.dot((abs_y *(20 + abs_y) * inv_p10abs_y * inv_p10abs_y).T )/T Edgx = np.sum(200 * sign_y * inv_p10abs_y * inv_p10abs_y * inv_p10abs_y)/T else : @@ -619,13 +608,13 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: vEGx = 2 * (EGx[4] > nf5['critical_point']) -1 elif max_i == 6: # G7(x) = x / (1 + x**2) - if fastica_on == True : + if fastica_on : grad = X.dot(((1 - yy)* inv_pabs_yy**2).T )/T Edgx = np.sum(2 * y * (yy-3)* inv_pabs_yy* inv_pabs_yy* inv_pabs_yy)/T else : grad = X.dot((weight * (1 - yy) * inv_pabs_yy**2 ).T ) / np.sum(weight) vEGx = 2 * (EGx[6] > nf7['critical_point']) -1 - if fastica_on == True : + if fastica_on : w1 = grad - Edgx * w else : grad = vEGx * grad @@ -649,12 +638,11 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: min_cost_queue[iter] = np.copy(min_cost) - if fastica_on == True : + if fastica_on : if cost_increaser_counter >= max_cost_increase_number or 1- np.min(np.abs(np.diag(W.dot(last_W.T)))) < tolerance: cost_increaser_counter = 0 W = np.copy(best_W ) last_W = np.copy(W) - iter_fastica = iter fastica_on = False continue else : @@ -678,7 +666,6 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: best_W = np.copy(W) Cost = np.zeros((max_iter_nonorth, 1)) min_cost_queue = min_cost * np.ones((max_iter_nonorth, 1)) - error = np.inf mu = 1 / 25 min_mu = 1/ 200 max_cost_increase_number = 3 @@ -848,7 +835,6 @@ def ICA_EBM(X: np.ndarray) -> np.ndarray: W = best_W W = W.dot(P) - # if show cost: to do later return W @@ -871,20 +857,19 @@ def simplified_ppval(pp: dict, xs: float) -> float: """ b = pp['breaks'][0] c = pp['coefs'] - l = int(pp['pieces'] ) + l_pieces = int(pp['pieces'] ) k = 4 - dd = 1 # find index index = float('nan ') middle_index = float('nan ') - if xs > b[l-1]: - index = l-1 + if xs > b[l_pieces-1]: + index = l_pieces-1 else: if xs < b[1]: index = 0 else : low_index = 0 - high_index = l-1 + high_index = l_pieces-1 while True : middle_index = int(np.ceil(((0.6* low_index + 0.4* high_index)))) @@ -933,7 +918,6 @@ def pre_processing(X: np.ndarray) -> tuple[np.ndarray, np.ndarray]: """ # pre-processing program - N = X.shape[0] T = X.shape[1] # remove DC Xmean = np.mean(X, axis=1) diff --git a/src/cedalion/sigdecomp/unimodal/ica_erbm.py b/src/cedalion/sigdecomp/unimodal/ica_erbm.py index 857070ca..a283eefc 100644 --- a/src/cedalion/sigdecomp/unimodal/ica_erbm.py +++ b/src/cedalion/sigdecomp/unimodal/ica_erbm.py @@ -39,24 +39,20 @@ def ICA_ERBM(X: np.ndarray, p: int = None ) -> np.ndarray: ################# Part 0: pre-processing ################# # load measuring functions as global variables - global nf1, nf2, nf3, nf4, nf5, nf6, nf7, nf8 - # table = np.load('measfunc_table.npy', allow_pickle= True) + global nf1, nf3, nf5, nf7 file_path = cedalion.data.get("measfunc_table.npy") table = np.load(file_path, allow_pickle=True) K = 8 - nf1, nf2, nf3, nf4, nf5, nf6, nf7, nf8 = ( - table[0], table[1], table[2], table[3], table[4], table[5], table[6], table[7] - ) + nf1, nf3, nf5, nf7 = table[0], table[2], table[4], table[6] - show_cost = False N, T = X.shape X, P = pre_processing(X) # initialize p if it is not provided if p is None: - p = int(np.min(11, T/ 50)) + p = int(np.minimum(11, T/ 50)) # initialize W W = ica_ebm.ICA_EBM(X) @@ -248,7 +244,7 @@ def ICA_ERBM(X: np.ndarray, p: int = None ) -> np.ndarray: min_cost = np.copy(Cost[iter-1]) best_W = np.copy(last_W) best_a = np.copy(a) - max_negentropy = np.copy(negentropy_array) + #max_negentropy = np.copy(negentropy_array) else: cost_increase_counter = cost_increase_counter + 1 @@ -318,7 +314,7 @@ def lfc(x: np.ndarray, p: int , choice, a0) -> tuple[np.ndarray, np.ndarray]: min_cost (np.ndarray, (1, 1)): the entropy rate estimation [1 x 1] """ - global nf1, nf2, nf3, nf4, nf5, nf6, nf7, nf8 + global nf1, nf3, nf5, nf7 tolerance = 1e-4 T = x.shape[0] X0 = sp.linalg.convolution_matrix(x, p, 'full').T @@ -516,20 +512,20 @@ def simplified_ppval(pp: dict, xs: float) -> float: b = pp['breaks'][0] c = pp['coefs'] - l = int(pp['pieces'] ) + l_pieces = int(pp['pieces'] ) k = 4 - dd = 1 + #dd = 1 # find index index = float('nan ') middle_index = float('nan ') - if xs > b[l-1]: - index = l-1 + if xs > b[l_pieces-1]: + index = l_pieces-1 else: if xs < b[1]: index = 0 else : low_index = 0 - high_index = l-1 + high_index = l_pieces-1 while True : middle_index = int(np.ceil(((0.6* low_index + 0.4* high_index)))) @@ -568,7 +564,7 @@ def cnstd_and_gain(a: np.ndarray) -> tuple[np.ndarray, np.ndarray]: # sample omega from 0 to pi n = 10*p h = np.pi / n - w = np.arange(0, n+1, 1) * h + #w = np.arange(0, n+1, 1) * h # calculate |A(w)|^2 Awr = np.zeros((1, n+1)) # real part From c73da3638247ff35263352d4007143d43d9e36b0 Mon Sep 17 00:00:00 2001 From: Eike Middell Date: Mon, 9 Feb 2026 22:15:39 +0100 Subject: [PATCH 2/4] included spa example into cedalion.data and notebook into docs --- docs/examples/Makefile | 1 + docs/machine_learning/index.rst | 3 +- .../53_constrained_ICA_example.ipynb | 481 ++++++++++++++++ .../constrained_ICA_example.ipynb | 523 ------------------ src/cedalion/data/__init__.py | 10 + 5 files changed, 493 insertions(+), 525 deletions(-) create mode 100644 examples/machine_learning/53_constrained_ICA_example.ipynb delete mode 100644 examples/machine_learning/constrained_ICA_example.ipynb diff --git a/docs/examples/Makefile b/docs/examples/Makefile index 32832bc8..c361fa3d 100644 --- a/docs/examples/Makefile +++ b/docs/examples/Makefile @@ -7,6 +7,7 @@ EXAMPLE_NOTEBOOKS = getting_started_io/00_test_installation.ipynb \ plots_visualization/12_plots_example.ipynb \ machine_learning/50_finger_tapping_lda_classification.ipynb \ machine_learning/52_ica_erbm_fingertapping_example.ipynb \ + machine_learning/53_constrained_ICA_example.ipynb \ modeling/31_glm_basis_functions.ipynb \ modeling/32_glm_fingertapping_example.ipynb \ modeling/33_glm_illustrative_example.ipynb \ diff --git a/docs/machine_learning/index.rst b/docs/machine_learning/index.rst index e63d4646..219c73b4 100644 --- a/docs/machine_learning/index.rst +++ b/docs/machine_learning/index.rst @@ -21,8 +21,7 @@ Decomposition Methods :recursive: :nosignatures: - cedalion.sigdecomp.ERBM - cedalion.sigdecomp.ICA_EBM + cedalion.sigdecomp.unimodal cedalion.sigdecomp.multimodal Examples diff --git a/examples/machine_learning/53_constrained_ICA_example.ipynb b/examples/machine_learning/53_constrained_ICA_example.ipynb new file mode 100644 index 00000000..98ad74aa --- /dev/null +++ b/examples/machine_learning/53_constrained_ICA_example.ipynb @@ -0,0 +1,481 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0", + "metadata": {}, + "outputs": [], + "source": [ + "# This cells setups the environment when executed in Google Colab.\n", + "try:\n", + " import google.colab\n", + " !curl -s https://raw.githubusercontent.com/ibs-lab/cedalion/dev/scripts/colab_setup.py -o colab_setup.py\n", + " # Select branch with --branch \"branch name\" (default is \"dev\")\n", + " %run colab_setup.py\n", + "except ImportError:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "import cedalion\n", + "import cedalion.sigproc.quality as quality\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import scipy as sp\n", + "import xarray as xr\n", + "from cedalion import units\n", + "from cedalion.sigdecomp.multimodal import arc_ebm, arc_erbm\n", + "import cedalion.data" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "# Constrained Independent Component Analysis (ICA)\n", + "\n", + "In this notebook, we demonstrate how constrained ICA methods can be applied to separate physiological sources from resting-state fNIRS data using auxiliary measurements. Specifically, we focus on adaptive-reverse constrained ICA-ERBM (arc-ERBM) and adaptive-reverse constrained ICA-EBM (arc-EBM).\n", + "\n", + "arc-ERBM and arc-EBM are constrained versions of the methods [Independent Component Analysis by Entropy Rate Bound Minimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6845364) (ICA-ERBM) and [by Entropy Bound Minimization](https://ieeexplore.ieee.org/abstract/document/5499122) (ICA-EBM). The general assumption in [Independent Component Analysis](https://en.wikipedia.org/wiki/Independent_component_analysis) is that the dataset $X \\in \\mathbb R^{N\\times T}$, with $N$ channels and $T$ sample points, is generated from a set of independent latent sources $S \\in \\mathbb R^{N\\times T}$, mixed by an unknown mixing matrix $A \\in \\mathbb R^{N \\times N}$.\n", + "\n", + "$$\n", + "X = A \\cdot S.\n", + "$$\n", + "\n", + "ICA methods aim to undo this mixing by determining a demixing matrix $W \\in \\mathbb{R}^{N \\times N}$, such that the estimated underlying sources $Y = W \\cdot X$ are maximally independent. The optimization of the demixing matrix is based on minimizing the mutual information $I$ in the case of ICA-EBM, and the mutual information rate $I_r$ in the case of ICA-ERBM. In both methods, this is done by minimizing a cost function $J$ that is equivalent to either $I$ or $I_r$ for each row vector $w_n$, $n = 1, ..., N$.\n", + "\n", + "In the constrained methods arc-EBM and arc-ERBM, we assume that there are $M \\leq N$ reference signals $r_n$, $n = 1, ..., M$, that correspond to $M$ latent sources. For each source estimate $y_n = w_n^T X$ that corresponds to a reference signal, the minimization of the cost function $J$ is extended through a constraint that uses a reference signal $r_n$:\n", + "\n", + "$$\n", + "\\min_{w_n} J(w_n) \\quad \\text{s.t.} \\quad \\varepsilon(r_n, y_n) \\geq \\rho_n\n", + "$$\n", + "\n", + "Here, $\\varepsilon$ is a similarity measure that operates in the frequency domain and enforces similar spectral characteristics between the source estimate $y_n$ and the reference signal $r_n$.\n", + "\n", + "In the following example, $X$ represents our resting-state fNIRS data, and as reference signals $r_n$, we use auxiliary PPG, respiration, and mean arterial pressure (MAP) measurements. After applying the constrained ICA methods and obtaining $W$, we can compute estimates of the separated sources as $y_n = w_n^T X$.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "## Loading Resting-State fNIRS Data\n", + "\n", + "We load the resting-state fNIRS data, including the auxiliary physiological measurements from the SNIRF file. For the demixing problem, we use middle-distance channels of approximately 15 mm in length to ensure that physiological noise signals are present in the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "# Load data\n", + "rec = cedalion.data.get_spa_fnirs()\n", + " \n", + "# Read fnirs data \n", + "fnirs_amp = rec['amp']\n", + "\n", + "\n", + "# Define middle distance channels \n", + "middle_channels = ['S1D7', 'S1D8', 'S1D13', 'S1D14', 'S1D15', 'S1D16', 'S2D8', 'S2D11', 'S2D12', \n", + " 'S3D7', 'S3D9', 'S3D10', 'S4D1', 'S4D5', 'S4D10', 'S4D16', 'S5D4', 'S5D5', 'S5D11', \n", + " 'S5D15', 'S6D3', 'S6D6', 'S6D12', 'S6D14', 'S7D2', 'S7D6', 'S7D9', 'S7D13', 'S8D22', \n", + " 'S8D23', 'S8D24', 'S8D29', 'S8D30', 'S8D31', 'S9D24', 'S9D27', 'S9D28', 'S10D23', 'S10D25',\n", + " 'S10D26', 'S11D19', 'S11D26', 'S11D31', 'S12D18', 'S12D19', 'S12D22', 'S12D28', 'S13D17', \n", + " 'S13D20', 'S13D27', 'S13D29', 'S14D20', 'S14D21', 'S14D25', 'S14D30']\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot three middle distance channels\n", + "fig, ax = plt.subplots(3, 1, sharex=True, figsize=(10, 5))\n", + "for i, ch in enumerate(['S1D7', 'S1D8', 'S1D13']):\n", + " ax[i].plot(fnirs_amp.time, fnirs_amp.sel(channel=ch, wavelength=\"760\"), \"r-\", label=\"760nm\")\n", + " ax[i].plot(fnirs_amp.time, fnirs_amp.sel(channel=ch, wavelength=\"850\"), \"b-\", label=\"850nm\")\n", + " ax[i].set_title(f\"Channel {ch}\")\n", + "\n", + "ax[0].legend()\n", + "ax[2].set_xlim(2400,2500)\n", + "ax[2].set_xlabel(\"Time (seconds)\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "## Conversion to Optical Density" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert to OD\n", + "fnirs_od = cedalion.nirs.cw.int2od(fnirs_amp)" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "## Select Resting-State Session\n", + "\n", + "Our data contain a resting-state session of 75 seconds. We select this session and crop the first 10 seconds to remove non-stationarities in the data. From the remaining session, we select a 60-second interval for our analysis using the middle-distance channels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "# Select the onset of the resting state interval (pre_sitting) \n", + "onset_resting = rec.stim[rec.stim.trial_type == 'pre_sitting'].onset.values[0]\n", + "\n", + "# We cropp the first 10 seconds of the resting state interval to \n", + "# avoid transient effects and select a 60 second interval for the analysis.\n", + "interval = [onset_resting + 10, onset_resting + 70]\n", + "\n", + "# Select interval and channels \n", + "interval_fnirs_od = fnirs_od.sel(time=slice(interval[0], interval[1]))\n", + "interval_fnirs_od = interval_fnirs_od.sel(channel= middle_channels)" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "## Channel Quality Assessment and Pruning\n", + "\n", + "We compute the Scalp Coupling Index (SCI) and Peak Spectral Power (PSP) for channel quality assessment. SCI and PSP are computed for each channel, and we then select the 40 channels with the highest percentage of clean time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "# Define parameters for quality metrics\n", + "window_length = 5 * units.s\n", + "sci_thresh = 0.75\n", + "psp_thresh = 0.1\n", + "sci_psp_percentage_thresh = 0.75\n", + "\n", + "# Compute SCI and PSP \n", + "sci, sci_mask = quality.sci(interval_fnirs_od, window_length, sci_thresh)\n", + "psp, psp_mask = quality.psp(interval_fnirs_od, window_length, psp_thresh)\n", + "sci_x_psp_mask = sci_mask & psp_mask\n", + "perc_time_clean = sci_x_psp_mask.sum(dim=\"time\") / len(sci.time)\n", + "\n", + "# Set the number of channels to include in the ICA analysis\n", + "num_ch = 40\n", + "\n", + "# Select the best channels \n", + "id_best_channels = np.argsort(perc_time_clean)[-num_ch:]\n", + "best_channels = id_best_channels['channel']\n", + "best_middle_channels = interval_fnirs_od.sel(channel=best_channels)" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "## Convert Optical Density to Concentration Changes " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert optical density to concentration changes \n", + "montage = rec.geo3d\n", + "dpf = xr.DataArray(\n", + " [6, 6],\n", + " dims=\"wavelength\",\n", + " coords={\"wavelength\": fnirs_od.wavelength},)\n", + " \n", + "fnirs_con = cedalion.nirs.cw.od2conc(fnirs_od, montage, dpf)" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "## High-Pass Filtering and Selection of HbO" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "# Apply high-pass filter\n", + "y_filt = fnirs_con.cd.freq_filter(fmin= 0.01, fmax= 0, butter_order=4)\n", + "\n", + "# Select resting state interval\n", + "y_filt = y_filt.sel(time = slice(interval[0], interval[1]))\n", + "\n", + "# Select middle distance channels\n", + "y_filt = y_filt.sel(channel=best_middle_channels.channel.values)\n", + "\n", + "# Select only HbO signal \n", + "y_filt = y_filt.sel(chromo = 'HbO')\n", + "\n", + "# Turn to numpy array \n", + "data = y_filt.values" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "## Prepare the Auxiliary Signals\n", + "\n", + "We now extract the respiration ('Resp'), PPG ('Pleth'), or mean arterial pressure ('MAP') signals from the recording. These signals must be downsampled to match the fNIRS sampling frequency. We first select the resting-state interval with an additional buffer and apply a band-pass filter to the data to avoid aliasing effects. The MAP signal may contain missing samples, which we address using an interpolation step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [], + "source": [ + "# Select the auxiliary signal from the recording \n", + "aux_name = 'Resp' # use 'MAP', 'Pleth' or 'Resp' \n", + "aux_signal = rec.aux_ts[aux_name]\n", + "\n", + "# Select the interval of the auxiliary signal with a 100 second buffer \n", + "# before and after the resting state interval to avoid edge effects in the filtering step\n", + "buffer = 100 \n", + "aux_signal = aux_signal.sel(time = slice(interval[0]- buffer, interval[1] + buffer ))\n", + "\n", + "# Add a new coordinate called samples and add unit \n", + "aux_signal['time'].attrs['units'] = 'seconds'\n", + "samples = np.arange(aux_signal.sizes['time'])\n", + "aux_signal = aux_signal.assign_coords(samples=('time', samples))\n", + "\n", + "# Fix missing samples in the MAP signal \n", + "if aux_name == 'MAP':\n", + " aux_signal = aux_signal.interpolate_na(dim = 'time' ,method = 'cubic',\n", + " fill_value='extrapolate')\n", + "\n", + "# Apply bandpass filter to the auxiliary signal to avaoid aliasing effects after the downsampling step.\n", + "aux_signal = aux_signal.cd.freq_filter(fmin= 0.01, fmax= 2.5 , butter_order=4) \n", + "\n", + "# Downsample the auxiliary signal by interpolating it to the time points of the fNIRS signal\n", + "time_line = fnirs_con.sel(time = slice(interval[0]- buffer,interval[1]+buffer))\n", + "aux_signal = aux_signal.drop_duplicates(dim='time')\n", + "aux_signal = aux_signal.interp(time=time_line.time)\n", + "aux_signal = aux_signal.dropna(dim=\"time\", how=\"any\")\n", + "\n", + "# Remove buffer \n", + "aux_signal = aux_signal.sel(time = slice(interval[0], interval[1]))\n", + "\n", + "# Turn to numpy array and reshape \n", + "aux_signal = np.array(aux_signal.values, dtype=np.float64).T\n", + "aux_signal = aux_signal.reshape(1, -1) \n" + ] + }, + { + "cell_type": "markdown", + "id": "18", + "metadata": {}, + "source": [ + "## Z-Transform Normalization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], + "source": [ + "# z-transform the data and auxiliary signal\n", + "data = sp.stats.zscore(data, axis=1) \n", + "aux_signal = sp.stats.zscore(aux_signal, axis=1) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the data and the auxiliary signal\n", + "fig, ax = plt.subplots(2, 1, figsize=(10, 5))\n", + "\n", + "x_time = np.arange(data.shape[1]) * 1/(7.4)\n", + "ax[0].plot(x_time, data.T)\n", + "ax[0].set_title('fNIRS data (HbO)')\n", + "ax[1].plot(x_time, aux_signal[0])\n", + "ax[1].set_title(f'Auxiliary signal ({aux_name})')\n", + "ax[1].set_xlabel('Time (seconds)')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "21", + "metadata": {}, + "source": [ + "## Apply Constrained ICA Methods\n", + "\n", + "We define a frequency reference signal by computing the power spectral density of the reference signal. We then apply the constrained ICA methods to the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the time domain and frequency domain reference signals \n", + "ref = np.copy(aux_signal)\n", + "ref_psd = (2/ ref.shape[1] ) * np.abs(np.fft.rfft(ref, axis = 1 )**2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the filter length for arc-ERBM \n", + "p = 11\n", + "\n", + "# Apply ICA methods \n", + "W1 = arc_erbm.arc_erbm(data, ref_psd.T, p)\n", + "W2 = arc_erbm.arc_erbm(data, ref_psd.T, p, ref.T)\n", + "W3 = arc_ebm.arc_ebm(data, ref_psd.T, 'psd')" + ] + }, + { + "cell_type": "markdown", + "id": "24", + "metadata": {}, + "source": [ + "## Compute Source Estimates\n", + "\n", + "For each constrained method, the first row of the demixing matrix corresponds to the referenced source. We therefore select the first row and compute the source estimate as $y = w_0^T X$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the estimated sources\n", + "source_arc_erbm = W1[0].dot(data)\n", + "source_arc_erbm_pr = W2[0].dot(data)\n", + "source_arc_ebm = W3[0].dot(data)\n", + "\n", + "# z-transform the estimated sources\n", + "source_arc_erbm = sp.stats.zscore(source_arc_erbm)\n", + "source_arc_erbm_pr = sp.stats.zscore(source_arc_erbm_pr)\n", + "source_arc_ebm = sp.stats.zscore(source_arc_ebm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot source estimates and reference signal\n", + "fig, ax = plt.subplots(3, 1, figsize=(10, 7))\n", + "\n", + "estimates = [source_arc_erbm, source_arc_erbm_pr, source_arc_ebm]\n", + "labels = ['arc-ERBM estimate', 'arc-ERBM (PR) estimate', 'arc-EBM estimate']\n", + "for i in range(3):\n", + " # Compute peak cross correlation for +/- 2 seconds lag \n", + " lags = np.arange(-15, 16, 1)\n", + " cross_corr = [np.corrcoef(ref, np.roll(estimates[i], lag, axis=0))[0, 1] for lag in lags]\n", + " max_corr = np.max(np.abs(cross_corr))\n", + " best_lag = lags[np.argmax(np.abs(cross_corr))]\n", + "\n", + " # Copmute RMSE between reference and estimate \n", + " rmse = np.sqrt(np.mean((ref - np.roll(estimates[i], best_lag, axis=0))**2)) \n", + " ax[i].set_title(f'{labels[i]} (correlation with reference: {max_corr:.2f}, RMSE: {rmse:.2f} )', \n", + " fontsize=10)\n", + "\n", + " # Plot estimate and reference \n", + " signal = np.roll(estimates[i], best_lag, axis=0)\n", + " signal = np.sign(np.corrcoef(ref, signal)[0, 1]) * signal\n", + " ax[i].plot(x_time, signal, label = labels[i])\n", + " ax[i].plot(x_time, ref.T, label = 'Reference signal', alpha = 0.7)\n", + " ax[i].legend( loc='upper left', bbox_to_anchor=(1, 1))\n", + "\n", + "\n", + "ax[2].set_xlabel('Time (seconds)')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cedalion_250922", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/machine_learning/constrained_ICA_example.ipynb b/examples/machine_learning/constrained_ICA_example.ipynb deleted file mode 100644 index 7028b10a..00000000 --- a/examples/machine_learning/constrained_ICA_example.ipynb +++ /dev/null @@ -1,523 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "6e3f1d9b", - "metadata": {}, - "outputs": [], - "source": [ - "# This cells setups the environment when executed in Google Colab.\n", - "try:\n", - " import google.colab\n", - " !curl -s https://raw.githubusercontent.com/ibs-lab/cedalion/dev/scripts/colab_setup.py -o colab_setup.py\n", - " # Select branch with --branch \"branch name\" (default is \"dev\")\n", - " %run colab_setup.py\n", - "except ImportError:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "5e7afeda", - "metadata": {}, - "outputs": [], - "source": [ - "import cedalion\n", - "import cedalion.sigproc.quality as quality\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import scipy as sp\n", - "import xarray as xr\n", - "from cedalion import units\n", - "from cedalion.sigdecomp.multimodal import arc_ebm, arc_erbm" - ] - }, - { - "cell_type": "markdown", - "id": "27652e3b", - "metadata": {}, - "source": [ - "# Constrained Independent Component Analysis (ICA)\n", - "\n", - "In this notebook, we demonstrate how constrained ICA methods can be applied to separate physiological sources from resting-state fNIRS data using auxiliary measurements. Specifically, we focus on adaptive-reverse constrained ICA-ERBM (arc-ERBM) and adaptive-reverse constrained ICA-EBM (arc-EBM).\n", - "\n", - "arc-ERBM and arc-EBM are constrained versions of the methods [Independent Component Analysis by Entropy Rate Bound Minimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6845364) (ICA-ERBM) and [by Entropy Bound Minimization](https://ieeexplore.ieee.org/abstract/document/5499122) (ICA-EBM). The general assumption in [Independent Component Analysis](https://en.wikipedia.org/wiki/Independent_component_analysis) is that the dataset $X \\in \\mathbb R^{N\\times T}$, with $N$ channels and $T$ sample points, is generated from a set of independent latent sources $S \\in \\mathbb R^{N\\times T}$, mixed by an unknown mixing matrix $A \\in \\mathbb R^{N \\times N}$.\n", - "\n", - "$$\n", - "X = A \\cdot S.\n", - "$$\n", - "\n", - "ICA methods aim to undo this mixing by determining a demixing matrix $W \\in \\mathbb{R}^{N \\times N}$, such that the estimated underlying sources $Y = W \\cdot X$ are maximally independent. The optimization of the demixing matrix is based on minimizing the mutual information $I$ in the case of ICA-EBM, and the mutual information rate $I_r$ in the case of ICA-ERBM. In both methods, this is done by minimizing a cost function $J$ that is equivalent to either $I$ or $I_r$ for each row vector $w_n$, $n = 1, ..., N$.\n", - "\n", - "In the constrained methods arc-EBM and arc-ERBM, we assume that there are $M \\leq N$ reference signals $r_n$, $n = 1, ..., M$, that correspond to $M$ latent sources. For each source estimate $y_n = w_n^T X$ that corresponds to a reference signal, the minimization of the cost function $J$ is extended through a constraint that uses a reference signal $r_n$:\n", - "\n", - "$$\n", - "\\min_{w_n} J(w_n) \\quad \\text{s.t.} \\quad \\varepsilon(r_n, y_n) \\geq \\rho_n\n", - "$$\n", - "\n", - "Here, $\\varepsilon$ is a similarity measure that operates in the frequency domain and enforces similar spectral characteristics between the source estimate $y_n$ and the reference signal $r_n$.\n", - "\n", - "In the following example, $X$ represents our resting-state fNIRS data, and as reference signals $r_n$, we use auxiliary PPG, respiration, and mean arterial pressure (MAP) measurements. After applying the constrained ICA methods and obtaining $W$, we can compute estimates of the separated sources as $y_n = w_n^T X$.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "76b4ea3e", - "metadata": {}, - "source": [ - "## Loading Resting-State fNIRS Data\n", - "\n", - "We load the resting-state fNIRS data, including the auxiliary physiological measurements from the SNIRF file. For the demixing problem, we use middle-distance channels of approximately 15 mm in length to ensure that physiological noise signals are present in the data." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "29062793", - "metadata": {}, - "outputs": [], - "source": [ - "# Load data\n", - "file_path = cedalion.data.get('spafNIRS_example_sub179.snirf')\n", - "rec = cedalion.io.read_snirf(file_path)[0]\n", - " \n", - "# Read fnirs data \n", - "fnirs_amp = rec['amp']\n", - "\n", - "\n", - "# Define middle distance channels \n", - "middle_channels = ['S1D7', 'S1D8', 'S1D13', 'S1D14', 'S1D15', 'S1D16', 'S2D8', 'S2D11', 'S2D12', \n", - " 'S3D7', 'S3D9', 'S3D10', 'S4D1', 'S4D5', 'S4D10', 'S4D16', 'S5D4', 'S5D5', 'S5D11', \n", - " 'S5D15', 'S6D3', 'S6D6', 'S6D12', 'S6D14', 'S7D2', 'S7D6', 'S7D9', 'S7D13', 'S8D22', \n", - " 'S8D23', 'S8D24', 'S8D29', 'S8D30', 'S8D31', 'S9D24', 'S9D27', 'S9D28', 'S10D23', 'S10D25',\n", - " 'S10D26', 'S11D19', 'S11D26', 'S11D31', 'S12D18', 'S12D19', 'S12D22', 'S12D28', 'S13D17', \n", - " 'S13D20', 'S13D27', 'S13D29', 'S14D20', 'S14D21', 'S14D25', 'S14D30']\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "92879701", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot three middle distance channels\n", - "fig, ax = plt.subplots(3, 1, sharex=True, figsize=(10, 5))\n", - "for i, ch in enumerate(['S1D7', 'S1D8', 'S1D13']):\n", - " ax[i].plot(fnirs_amp.time, fnirs_amp.sel(channel=ch, wavelength=\"760\"), \"r-\", label=\"760nm\")\n", - " ax[i].plot(fnirs_amp.time, fnirs_amp.sel(channel=ch, wavelength=\"850\"), \"b-\", label=\"850nm\")\n", - " ax[i].set_title(f\"Channel {ch}\")\n", - "\n", - "ax[0].legend()\n", - "ax[2].set_xlim(2400,2500)\n", - "ax[2].set_xlabel(\"Time (seconds)\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "8067fcc6", - "metadata": {}, - "source": [ - "## Conversion to Optical Density" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b520c07c", - "metadata": {}, - "outputs": [], - "source": [ - "# Convert to OD\n", - "fnirs_od = cedalion.nirs.cw.int2od(fnirs_amp)" - ] - }, - { - "cell_type": "markdown", - "id": "01f689ae", - "metadata": {}, - "source": [ - "## Select Resting-State Session\n", - "\n", - "Our data contain a resting-state session of 75 seconds. We select this session and crop the first 10 seconds to remove non-stationarities in the data. From the remaining session, we select a 60-second interval for our analysis using the middle-distance channels." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "07e3caeb", - "metadata": {}, - "outputs": [], - "source": [ - "# Select the onset of the resting state interval (pre_sitting) \n", - "onset_resting = rec.stim[rec.stim.trial_type == 'pre_sitting'].onset.values[0]\n", - "\n", - "# We cropp the first 10 seconds of the resting state interval to \n", - "# avoid transient effects and select a 60 second interval for the analysis.\n", - "interval = [onset_resting + 10, onset_resting + 70]\n", - "\n", - "# Select interval and channels \n", - "interval_fnirs_od = fnirs_od.sel(time=slice(interval[0], interval[1]))\n", - "interval_fnirs_od = interval_fnirs_od.sel(channel= middle_channels)" - ] - }, - { - "cell_type": "markdown", - "id": "191a3a64", - "metadata": {}, - "source": [ - "## Channel Quality Assessment and Pruning\n", - "\n", - "We compute the Scalp Coupling Index (SCI) and Peak Spectral Power (PSP) for channel quality assessment. SCI and PSP are computed for each channel, and we then select the 40 channels with the highest percentage of clean time." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "763332d1", - "metadata": {}, - "outputs": [], - "source": [ - "# Define parameters for quality metrics\n", - "window_length = 5 * units.s\n", - "sci_thresh = 0.75\n", - "psp_thresh = 0.1\n", - "sci_psp_percentage_thresh = 0.75\n", - "\n", - "# Compute SCI and PSP \n", - "sci, sci_mask = quality.sci(interval_fnirs_od, window_length, sci_thresh)\n", - "psp, psp_mask = quality.psp(interval_fnirs_od, window_length, psp_thresh)\n", - "sci_x_psp_mask = sci_mask & psp_mask\n", - "perc_time_clean = sci_x_psp_mask.sum(dim=\"time\") / len(sci.time)\n", - "\n", - "# Set the number of channels to include in the ICA analysis\n", - "num_ch = 40\n", - "\n", - "# Select the best channels \n", - "id_best_channels = np.argsort(perc_time_clean)[-num_ch:]\n", - "best_channels = id_best_channels['channel']\n", - "best_middle_channels = interval_fnirs_od.sel(channel=best_channels)" - ] - }, - { - "cell_type": "markdown", - "id": "9a0d3ade", - "metadata": {}, - "source": [ - "## Convert Optical Density to Concentration Changes " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b42fd6ad", - "metadata": {}, - "outputs": [], - "source": [ - "# Convert optical density to concentration changes \n", - "montage = rec.geo3d\n", - "dpf = xr.DataArray(\n", - " [6, 6],\n", - " dims=\"wavelength\",\n", - " coords={\"wavelength\": fnirs_od.wavelength},)\n", - " \n", - "fnirs_con = cedalion.nirs.cw.od2conc(fnirs_od, montage, dpf)" - ] - }, - { - "cell_type": "markdown", - "id": "4aa379df", - "metadata": {}, - "source": [ - "## High-Pass Filtering and Selection of HbO" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "79b33142", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/miniconda3/envs/cedalion/lib/python3.11/site-packages/xarray/core/variable.py:315: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " data = np.asarray(data)\n" - ] - } - ], - "source": [ - "# Apply high-pass filter\n", - "y_filt = fnirs_con.cd.freq_filter(fmin= 0.01, fmax= 0, butter_order=4)\n", - "\n", - "# Select resting state interval\n", - "y_filt = y_filt.sel(time = slice(interval[0], interval[1]))\n", - "\n", - "# Select middle distance channels\n", - "y_filt = y_filt.sel(channel=best_middle_channels.channel.values)\n", - "\n", - "# Select only HbO signal \n", - "y_filt = y_filt.sel(chromo = 'HbO')\n", - "\n", - "# Turn to numpy array \n", - "data = y_filt.values" - ] - }, - { - "cell_type": "markdown", - "id": "0cff03d6", - "metadata": {}, - "source": [ - "## Prepare the Auxiliary Signals\n", - "\n", - "We now extract the respiration ('Resp'), PPG ('Pleth'), or mean arterial pressure ('MAP') signals from the recording. These signals must be downsampled to match the fNIRS sampling frequency. We first select the resting-state interval with an additional buffer and apply a band-pass filter to the data to avoid aliasing effects. The MAP signal may contain missing samples, which we address using an interpolation step." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d4fca2e0", - "metadata": {}, - "outputs": [], - "source": [ - "# Select the auxiliary signal from the recording \n", - "aux_name = 'Resp' # use 'MAP', 'Pleth' or 'Resp' \n", - "aux_signal = rec.aux_ts[aux_name]\n", - "\n", - "# Select the interval of the auxiliary signal with a 100 second buffer \n", - "# before and after the resting state interval to avoid edge effects in the filtering step\n", - "buffer = 100 \n", - "aux_signal = aux_signal.sel(time = slice(interval[0]- buffer, interval[1] + buffer ))\n", - "\n", - "# Add a new coordinate called samples and add unit \n", - "aux_signal['time'].attrs['units'] = 'seconds'\n", - "samples = np.arange(aux_signal.sizes['time'])\n", - "aux_signal = aux_signal.assign_coords(samples=('time', samples))\n", - "\n", - "# Fix missing samples in the MAP signal \n", - "if aux_name == 'MAP':\n", - " aux_signal = aux_signal.interpolate_na(dim = 'time' ,method = 'cubic',\n", - " fill_value='extrapolate')\n", - "\n", - "# Apply bandpass filter to the auxiliary signal to avaoid aliasing effects after the downsampling step.\n", - "aux_signal = aux_signal.cd.freq_filter(fmin= 0.01, fmax= 2.5 , butter_order=4) \n", - "\n", - "# Downsample the auxiliary signal by interpolating it to the time points of the fNIRS signal\n", - "time_line = fnirs_con.sel(time = slice(interval[0]- buffer,interval[1]+buffer))\n", - "aux_signal = aux_signal.drop_duplicates(dim='time')\n", - "aux_signal = aux_signal.interp(time=time_line.time)\n", - "aux_signal = aux_signal.dropna(dim=\"time\", how=\"any\")\n", - "\n", - "# Remove buffer \n", - "aux_signal = aux_signal.sel(time = slice(interval[0], interval[1]))\n", - "\n", - "# Turn to numpy array and reshape \n", - "aux_signal = np.array(aux_signal.values, dtype=np.float64).T\n", - "aux_signal = aux_signal.reshape(1, -1) \n" - ] - }, - { - "cell_type": "markdown", - "id": "861ab352", - "metadata": {}, - "source": [ - "## Z-Transform Normalization" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c751fe84", - "metadata": {}, - "outputs": [], - "source": [ - "# z-transform the data and auxiliary signal\n", - "data = sp.stats.zscore(data, axis=1) \n", - "aux_signal = sp.stats.zscore(aux_signal, axis=1) " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "739b1540", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the data and the auxiliary signal\n", - "fig, ax = plt.subplots(2, 1, figsize=(10, 5))\n", - "\n", - "x_time = np.arange(data.shape[1]) * 1/(7.4)\n", - "ax[0].plot(x_time, data.T)\n", - "ax[0].set_title('fNIRS data (HbO)')\n", - "ax[1].plot(x_time, aux_signal[0])\n", - "ax[1].set_title(f'Auxiliary signal ({aux_name})')\n", - "ax[1].set_xlabel('Time (seconds)')\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "8a9159f2", - "metadata": {}, - "source": [ - "## Apply Constrained ICA Methods\n", - "\n", - "We define a frequency reference signal by computing the power spectral density of the reference signal. We then apply the constrained ICA methods to the data." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "dbccf84b", - "metadata": {}, - "outputs": [], - "source": [ - "# Create the time domain and frequency domain reference signals \n", - "ref = np.copy(aux_signal)\n", - "ref_psd = (2/ ref.shape[1] ) * np.abs(np.fft.rfft(ref, axis = 1 )**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e21ae032", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the filter length for arc-ERBM \n", - "p = 11\n", - "\n", - "# Apply ICA methods \n", - "W1 = arc_erbm.arc_erbm(data, ref_psd.T, p)\n", - "W2 = arc_erbm.arc_erbm(data, ref_psd.T, p, ref.T)\n", - "W3 = arc_ebm.arc_ebm(data, ref_psd.T, 'psd')" - ] - }, - { - "cell_type": "markdown", - "id": "744d6ba9", - "metadata": {}, - "source": [ - "## Compute Source Estimates\n", - "\n", - "For each constrained method, the first row of the demixing matrix corresponds to the referenced source. We therefore select the first row and compute the source estimate as $y = w_0^T X$." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "862fee83", - "metadata": {}, - "outputs": [], - "source": [ - "# Compute the estimated sources\n", - "source_arc_erbm = W1[0].dot(data)\n", - "source_arc_erbm_pr = W2[0].dot(data)\n", - "source_arc_ebm = W3[0].dot(data)\n", - "\n", - "# z-transform the estimated sources\n", - "source_arc_erbm = sp.stats.zscore(source_arc_erbm)\n", - "source_arc_erbm_pr = sp.stats.zscore(source_arc_erbm_pr)\n", - "source_arc_ebm = sp.stats.zscore(source_arc_ebm)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "1c1c083e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot source estimates and reference signal\n", - "fig, ax = plt.subplots(3, 1, figsize=(10, 7))\n", - "\n", - "estimates = [source_arc_erbm, source_arc_erbm_pr, source_arc_ebm]\n", - "labels = ['arc-ERBM estimate', 'arc-ERBM (PR) estimate', 'arc-EBM estimate']\n", - "for i in range(3):\n", - " # Compute peak cross correlation for +/- 2 seconds lag \n", - " lags = np.arange(-15, 16, 1)\n", - " cross_corr = [np.corrcoef(ref, np.roll(estimates[i], lag, axis=0))[0, 1] for lag in lags]\n", - " max_corr = np.max(np.abs(cross_corr))\n", - " best_lag = lags[np.argmax(np.abs(cross_corr))]\n", - "\n", - " # Copmute RMSE between reference and estimate \n", - " rmse = np.sqrt(np.mean((ref - np.roll(estimates[i], best_lag, axis=0))**2)) \n", - " ax[i].set_title(f'{labels[i]} (correlation with reference: {max_corr:.2f}, RMSE: {rmse:.2f} )', \n", - " fontsize=10)\n", - "\n", - " # Plot estimate and reference \n", - " signal = np.roll(estimates[i], best_lag, axis=0)\n", - " signal = np.sign(np.corrcoef(ref, signal)[0, 1]) * signal\n", - " ax[i].plot(x_time, signal, label = labels[i])\n", - " ax[i].plot(x_time, ref.T, label = 'Reference signal', alpha = 0.7)\n", - " ax[i].legend( loc='upper left', bbox_to_anchor=(1, 1))\n", - "\n", - "\n", - "ax[2].set_xlabel('Time (seconds)')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cedalion", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/src/cedalion/data/__init__.py b/src/cedalion/data/__init__.py index 9b7f91d9..b2c9e000 100644 --- a/src/cedalion/data/__init__.py +++ b/src/cedalion/data/__init__.py @@ -57,6 +57,8 @@ "snirf2bids_example_dataset.zip" : "f14508e332c7d259c13b9717ac3c490ab2cabfd7b30fdf97b347d5ba59b783d1", # noqa:E501 "fieldtrip_standard1005.elc" : "sha256:1ee59197946d62de872db2ac7f2243a596662c231427366f6dc5d84ed237f853", # noqa:E501 + + "spafNIRS_example_sub179.zip" : "sha256:49f20b0ac608f3f752ca73e1a2a553a3b4f2e680c061505bcee132cca226daee", # noqa:E501 }, urls={ "fieldtrip_standard1005.elc" : "https://raw.githubusercontent.com/fieldtrip/fieldtrip/refs/heads/master/template/electrode/standard_1005.elc" @@ -300,3 +302,11 @@ def get_snirf2bids_example_dataset() -> tuple[Path, Path]: def get_fieldtrip_colin27_landmarks() -> cdt.LabeledPoints: fname = DATASETS.fetch("fieldtrip_standard1005.elc") return cedalion.io.read_fieldtrip_elc(fname) + + +def get_spa_fnirs() -> cdc.Recording: + fnames = DATASETS.fetch("spafNIRS_example_sub179.zip", processor=pooch.Unzip()) + fname = [Path(i) for i in fnames if i.endswith(".snirf")][0] + rec = cedalion.io.read_snirf(fname)[0] + + return rec From d5786753f25647c8896eac55b7165182bd814a1e Mon Sep 17 00:00:00 2001 From: Eike Middell Date: Fri, 13 Feb 2026 11:59:14 +0100 Subject: [PATCH 3/4] added README.md to spa_fnirs_example dataset --- src/cedalion/data/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cedalion/data/__init__.py b/src/cedalion/data/__init__.py index b2c9e000..1e7df754 100644 --- a/src/cedalion/data/__init__.py +++ b/src/cedalion/data/__init__.py @@ -58,7 +58,7 @@ "fieldtrip_standard1005.elc" : "sha256:1ee59197946d62de872db2ac7f2243a596662c231427366f6dc5d84ed237f853", # noqa:E501 - "spafNIRS_example_sub179.zip" : "sha256:49f20b0ac608f3f752ca73e1a2a553a3b4f2e680c061505bcee132cca226daee", # noqa:E501 + "spafNIRS_example_sub179.zip" : "sha256:0a247be5bfa3c7b5bc12d19203e2bd5432df964d72646945891601d0ba944141", # noqa:E501 }, urls={ "fieldtrip_standard1005.elc" : "https://raw.githubusercontent.com/fieldtrip/fieldtrip/refs/heads/master/template/electrode/standard_1005.elc" From 4089528466ae5801e29131ce9c11e83da02fc7ac Mon Sep 17 00:00:00 2001 From: Eike Middell Date: Fri, 13 Feb 2026 12:14:26 +0100 Subject: [PATCH 4/4] updated CHANGELOG.md --- docs/CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index 99bbc905..0f76d434 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -3,6 +3,7 @@ ## Unreleased changes (available on the `dev` branch) ### Added +- Added functionality and examples for constrained ICA methods (arc-ERBM, arc-EBM), by [Jacqueline Behrendt](https://github.com/jackybehrendt12). ([#133](https://github.com/ibs-lab/cedalion/pull/133)) - An example notebook for ICA source extraction was added, by [Jacqueline Behrendt](https://github.com/jackybehrendt12). ([#112](https://github.com/ibs-lab/cedalion/pull/112)) - Added `TwoSurfaceHeadmodel.scale_to_headsize` and `TwoSurfaceHeadmodel.scale_to_landmarks` to adjust the head model's size to the head circumferences or digitized landmarks, respectively. By [Eike Middell](https://github.com/emiddell).