Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding

This repository contains code for paper Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding

The data used in this study can be found on OpenNeuro.org.

Repository Organization

The code has been organized into the following functional categories:

stimuli/

• midi files of stimuli used in this project:
  • original_music
  • chimeric_music
  • The .wav format stimuli can be found at google drive folder here

regressors/

• downbeat_reg.plk - downbeat TRF analysis regressors
• music_exp_reg - melodic expectation TRF anlaysis regressors

eeg_analysis/

EEG Signal Processing & Analysis

• ICA_eyemovement.py - Independent Component Analysis for eye movement artifact removal
• derive_ERP_ICA.py - Event-Related Potential analysis with ICA preprocessing
• derive_ERP_ICA_downbeat.py - ERP analysis focused on downbeat processing

music_generation/

Music Stimulus Generation

• chimera_midi.py - Generate Chimera music by combining pitch and rhythm from different sources
• chimera_presentation.py - Present Chimera music stimuli in experiments
• midi_scramble.py - MIDI file scrambling utilities
• original_chimera_pairing.py - Pair original and Chimera music stimuli

trf_analysis/

Temporal Response Function (TRF) Analysis

• derive_TRF_ICA_downbeat.py - TRF analysis focused on downbeat processing
• derive_TRF_ICA_both_pitch-time.py - TRF analysis for pitch-time interactions
• derive_TRF_ICA_LTM-STM.py - Long-term vs Short-term memory TRF analysis
• TRF_analysis_all_LTM_STM.py - LTM-STM comparison analysis
• TRF_analysis_all_both_pitch-time.py - Pitch-time interaction analysis
• TRF_find_lambda_mode.py - Lambda parameter optimization using mode selection
• TRF_find_lambda_new_algorithm.py - Advanced lambda parameter optimization

abr_analysis/

Auditory Brainstem Response (ABR) Analysis

• derive_ABR_new.py - Updated ABR analysis with improved processing

statistics/

Statistical Analysis & Visualization

• preference_analysis.py - Analyze participant music preferences
• show_plot_publication.py - Generate publication-quality plots
• note_timing_dist.py - Analyze note timing distributions

revision_edit/

Revision edits

• Jneuro_edit_review.ipynb - revision edit analysis
• Jneuro_edit_review_plot.ipynb - Plotting for revision

MusicianshipQuestionnaire.pdf

• The musicianship questionnaire we used in this study (reference: Whiteford, K. L., Baltzell, L. S., Chiu, M., Cooper, J. K., Faucher, S., Goh, P. Y., ... & Oxenham, A. J. (2025). Large-scale multi-site study shows no association between musical training and early auditory neural sound encoding. Nature Communications, 16(1), 7152.)