This repo contains the code for the TMS-EEG preprocessing pipeline originally described in:
C.C. Cline, M.V. Lucas, Y. Sun, M. Menezes, A. Etkin. "Advanced Artifact Removal for Automated TMS-EEG Data Processing," 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), 2021, doi: 10.1109/NER49283.2021.9441147.
In brief, this pipeline consists of the following stages:
- Epoching
- Artifact interpolation (with custom autoregressive blending)
- Downsampling
- Baseline correction
- High-pass filtering
- Bad-channel identification
- Early eye-related IC rejection (added in v2.0.0)
- SOUND
- Decay component removal
- Artifact interpolation
- Line noise filtering
- ICA
- IC rejection with ICLabel and additional TMS-specific rejection rules
- Low-pass filtering
- Average rereferencing
This code assumes you have EEGLab installed at ~/Documents/MATLAB/eeglab on Windows or Mac, or ~/matlab/eeglab on Linux. It also assumes you have installed the ICLabel and TESA extensions in EEGLab.
Assuming you have downloaded this whole repo to a folder called AARATEPPipeline, add dependencies to your MATLAB path with
addpath('AARATEPPipeline');
addpath('AARATEPPipeline/Common');
addpath('AARATEPPipeline/Common/EEGAnalysisCode');
Load your data as an EEGLab struct EEG. For example:
[EEG, misc] = c_TMSEEG_prepareForPreprocessing(...
'inputFilePath', 'MyStudy/rawdata/RecordingName.vhdr',...
'epochTimespan', [-1 2]);
Then call the main preprocessing pipeline script:
EEG = c_TMSEEG_Preprocess_AARATEPPipeline(EEG,...
'pulseEvent', misc.pulseEvent,...
'epochTimespan', misc.epochTimespan,...
'outputDir', 'MyStudy/derivatives/RecordingName',...
'outputFilePrefix', 'RecordingName')
See individual scripts for additional available parameters.