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The code in this repository allows you to analyze MEG data with decoding analysis as in Isik et al., 2014. 
You can take MEG .fif files preprocess them with Brainstorm software (http://neuroimage.usc.edu/brainstorm/),  convert them into raster format (), and run decoding analysis using the Neural Decoding Toolbox (http://readout.info/)
 
Preprocess MEG data with Brainstorm
* Download/set brainstorm databse locations based according to tutorial
* File --> New protocol (individual or default anatomy)
* Run load_bandpass script while Brainstorm is open
e.g.: 
preprocess_with_brainstorm(Ô[MEG_raw_data_directory]/Subject01Õ, {Ôcbcl000.fifÕ, Ôcbcl000-1.fifÕ}, [-.2, .6]);

Convert Brainstorm .mat files to raster format
* Brainstorm saves processed .mat files in folder: brainstorm_db/<protocol_name>/data/<subject_name>/<trigger_number> 
* Run convert_to_raster.m to save pre-processed files in raster format for decoding toolbox (more information on this data format at readout.info).
e.g.:
convert_to_raster(Ô[brainstorm_directory]/brainstorm/brainstorm_dbÕ, ÔSubject01Õ, ÔSubject01_labels.matÕ, Ô[raster_directory]/Subjecto01_rastersÕ, 801);

Run decoding using neural decoding toolbox
* Download neural decoding toolbox (readout.info)
* Copy @avg_DS and @avg_generalization_DS to datasources folder in the toolbox directory
* Run run_decoding.m 
* For more information on decoding parameters, visit readout.info.
e.g. without invariance:

run_decoding(ÔSubject01Õ, [toolbox_path], [raster_directory], Ô[bin_directory]Õ, Ô[results_directory]Õ, Ôscene_decodingÕ, Ôstim_IDÕ, 1:25, 1:25, 5, 10, 5,5,10,25,5,1);

e.g. with invariance: 

run_decoding(ÔSubject04Õ, [toolbox_path], [raster_directory], Ô[bin_directory]Õ, Ô[results_directory]Õ, 'train_large_test_mid', 'size_pos_stim_ID', {111,112,113,114,115,116}, {211,212,213,214,215,216}, 5, 10, 5,5, 10, 25, 2, 1)


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