MatMef

Matlab wrapper for MEF 3.0 library

This project provides Matlab (mex) functions to read and write MEF 3.0 data using the MEF 3.0 library

Using pre-compiled mex files

1. Clone or download (and extract) the matmef repository
2. Use the functions (as illustrated in the examples below)

Building from source

1. Clone the matmef repository using: git clone https://github.com/MultimodalNeuroimagingLab/matmef.git

2. Start matlab and set the matmef folder as your working directory

3. To compile the .mex files, run the following lines in matlab:

   • mex read_mef_session_metadata.c matmef_mapping.c mex_utils.c matmef_dataconverter.c
   • mex read_mef_ts_data.c matmef_read.c mex_utils.c matmef_dataconverter.c
   • mex init_mef_struct.c matmef_mapping.c mex_utils.c matmef_dataconverter.c
   • mex write_mef_segment_metadata.c matmef_write.c mex_utils.c matmef_utils.c matmef_mapping.c matmef_dataconverter.c
   • mex write_mef_ts_segment_data.c matmef_write.c mex_utils.c matmef_utils.c matmef_mapping.c matmef_dataconverter.c

Additional requirements for MAC users prior to mex compilation above:

a) install xcode Open Mac App Store → Search for Xcode → Install. b) in terminal, switch to full Xcode developer directory: sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer

c) accept the license: sudo xcodebuild -license press (to display license): enter type: agree

d) verify MATLAB detects the compiler (back in matlab): mex -setup C --> You should now see an option like: Xcode with Clang

e) compile .mex files (see step 3 above)

Matlab usage examples

%  
% High-level  
%  

% metadata only  
[metadata] = readMef3('./mefSessionData/');

% two channels  
[metadata, signaldata] = readMef3('./mefSessionData/', [], {'Ch02', 'Ch07'});  

% all channels, samples 0-1000  
[metadata, signaldata] = readMef3('./mefSessionData/', [], [], 'samples', 0, 1000);  

% two channels, samples 0-1000  
[metadata, signaldata] = readMef3('./mefSessionData/', [], {'Ch02', 'Ch07'}, 'samples', 0, 1000);  

% all channels, multiple ranges/epochs
ranges = [[0,    1000]; ...
          [1000, 2000]; ...
          [5000, 6000]];
[metadata, signaldata] = readMef3('./mefSessionData/', [], [], 'samples', ranges);

%  
% Low-level  
%  
  
session = read_mef_session_metadata('./mefSessionData/', [], 1);  
data = read_mef_ts_data('./mefSessionData/channelPath/');  
data = read_mef_ts_data('./mefSessionData/channelPath/', [], 'samples', int64(0), int64(1000));
data = read_mef_ts_data('./mefSessionData/channelPath/', [], 'time', int64(1578715810000000), int64(1578715832000000));

Acknowledgements

• Written by Max van den Boom (Multimodal Neuroimaging Lab, Mayo Clinic, Rochester MN)

• Adapted from PyMef (by Jan Cimbalnik, Matt Stead, Ben Brinkmann, and Dan Crepeau)

• EEGLAB plugin code by Arnaud Delorme

• Contributors: Richard J. Cui, Dan Crepeau

• This project was funded by the National Institute Of Mental Health of the National Institutes of Health Award Number R01MH122258 to Dora Hermes