PreprocEEG

This code is related to the work presented in the EMBC 2022:

La Fisca and Gosselin, "A Hybrid Framework for ERP Preprocessing in EEG Experiments", 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 2022.

Description

This repository aims to preprocess ERP data from an EEG experiment (bdf, edf or mat file) using a hybrid framework that follows recommendations of OHBM COBIDAS MEEG committee. Bad channels, ocular artifacts, muscle artifacts and line noise are optimally reduced in the process.

Requirements

Matlab R2018+
FieldTrip toolbox (https://www.fieldtriptoolbox.org/download/)
ReMAE license (http://staff.ustc.edu.cn/~xunchen/resource.htm)

Download the repository

Clone this repository to a suitable local folder using the command shell:

cd YourLocalFolder
git clone https://github.com/numediart/PreprocEEG
cd PreprocEEG

Configure the framework

Edit the config.json file to adapt the framework to your data by following the provided template.

Provide data type and path:

{
    "datatype"         : "bdf",
    "comment_datatype" : "bdf, edf or mat",
    "eeg_path"         : "PATH_TO_EEG_BIDS_FOLDER",
    "eeg_filename"     : "sub-xxx_task-yyy_eeg",
    "output_path"      : null,

Provide event file with required information for the data segmentation:

    "event"         : "PATH_TO_EVENT_FILE/sub-xxx_task-yyy_event.mat",
    "comment_event" : "replace 'null' by the path of your event file if needed",

    "trial_function"        : "ft_trialfun_general",
    "comment_trial_function": "replace with the desired trial function (cf. FieldTrip doc)",
    "eventtype"             : "STATUS",
    "eventvalue"            : [10, 11, 12],
    "prestim"               : 0.5,
    "poststim"              : 1,

Provide general information about the process:

    "save_choice"           : false,
    "check_steps"           : true,
    "timelock_analysis"     : true,
    "check_timelock"        : true,

Define parameter values:

    "fs"                    : 2048,
    "fs_down"               : 512,
    "line_frequency"        : 50,
    "target_conditions"     : [1,8],

    "lowpass_freq"          : 200,

    "zapline_ncomponent"    : 3,

    "eemd_ensemble_number"  : 100,
    "eemd_noise_level"      : 0.2,
    "eemd_mode_number"      : -1,
    "eemd_treshold"         : 0.9,
    
    "cca_threshold"         : 0.5,
    "cca_time_lag"          : 1

Finally, save the modified config.json file.

Fit BIDS format

The provided code reads data following the BIDS format. Edit your dataset to fit this format. Example:

/
├── dataset_description.json
├── participants.tsv
├── README
├── CHANGES
├── sub-001
│   └── eeg
│       ├── sub-001_task-xxx_eeg.bdf
│       ├── sub-001_task-xxx_eeg.json
│       └── sub-001_task-xxx_events.tsv
├── sub-002
├── ...
├── derivatives
│   └── preproc_and_segment
│       ├── dataset_description.json
│       ├── sub-001
│       │   └── eeg
│       │       ├── sub-001_preprocessed.mat
│       │       ├── sub-001_timelock.mat
│       │       └── sub-001_timelock.json
│       ├── sub-002
│       └── ...

Perform the preprocessing

Open the script in Matlab editor and run it to process your data. The resulting preprocessed EEG files will be automatically stored in the "eeg_path" folder with the name "sub-xxx_task-xxx_eeg.mat" except if you specified another location as the "output_path" of the config file.