Face processing M/EEG data for Dynamic Causal Modelling (Faces vs Scrambled)

This dataset consists of BIDS-formatted M/EEG data from Wakeman &amp; Henson (2015) that have been processed for DCM analysis. Multimodal data from EEG, MEG magnetometers and MEG planar gradiometers from all 16 subjects have been processed with the analysis pipeline presented in Henson et al (2019). Individual subjects' T1 images were used for creating head models. Forward models (leadfields) for all subjects are included in the dataset. The file 'derivatives_dcm_ready_with_gainmat_all_subjects.zip' consists of dcm-ready data for all 16 subjects Each subject's data consists of 3 files: SPM sidecar data file: wmaMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg.dat SPM data header file: wmaMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg.mat Gain matrix (forward model): SPMgainmatrix_wmaMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg_1.mat *XX here indicates zero-padded subject number (01, 02, ...). Supplied filelist.txt contains the full filelist and folder hierarchy inside the compressed zip archive. <br> This dataset was prepared for demonstration of group-level estimation and inference of DCMs from M/EEG data. The raw data can be found here: https://openneuro.org/datasets/ds000117 Scripts used to produce this dataset from the raw data, as well as as scripts demonstrating group DCM analysis can be found here:https://github.com/pranaysy/cognestic22_multimodal_dcm <br> For more information or queries, please get in touch, or open an issue on the GitHub repository linked above. <br> Note: This dataset differs from https://figshare.com/articles/dataset/Face_processing_M_EEG_data_for_Dynamic_Causal_Modelling/21130297 in that this one has two conditions: famous and unfamiliar faces have been merged into one condition and scrambled faces. The linked one has three conditions: famous faces, unfamiliar faces and scrambled faces.