music_imagery_data

Data for the study reported in:

• Martinez, D. R. Q., Rubio, G. F., Bonetti, L., Achyutuni, K. G., Tzovara, A., Knight, R. T., & Vuust, P. (2024). 
  Decoding reveals the neural representation of perceived and imagined musical sounds (p. 2023.08.15.553456). 
  bioRxiv. https://doi.org/10.1101/2023.08.15.553456

Description

• demographics.csv: Participant demographics.
• decoding_accuracies: Time-generalized neural decoding accuracy per participant stored as Python dict containing 2d arrays (training_times x testing_times), with each entry indexing a condition or contrast of interest.
• decoding_patterns: Decoding patterns per participant obtained from model coefficients stored as dict containing mne.Epochs arrays indexed by condition or contrast name.
• epochs: Per participant single-trial epochs stored as dict containing mne.Epochs arrays after preprocessing (ica removed, high-pass >= 0.05Hz, smoothing with tstep= 25ms and twin=50ms, sfreq=40Hz).
• figures_data: Values plotted in each figure and/or the data necessary to obtain them. Excel sheets or csv.
• inverse_solutions: Inverse operator per participant to project sensor data into source space (mne inverse class).
• logs: Experiment log files. Recall=recognize, manipulation=invert.
• MNI_transforms: Transformation matrices per subject to convert source data into common MNI space.
• neural_accuracy: Diagonal neural decoding accuracies per subject and time-point, together with behavioral and demographic data.
• statistics: Statistical output for the different tests. Dictionaries with entries containing t-statisitcs, p-values, clusters, etc...