Easy-to-use EEG preprocessing pipeline based on MNE

This project can help you build an EEG data process pipeline that is easy to share.

For beginners, in order to preprocess EEG data, there is a lot of knowledge to know. At the same time, the pre-processing process is complicated, and even oneself may forget the details involved in preprocessing. This increases the difficulty for researchers to share scientific research results, and also makes it difficult to reproduce experimental results.

By using a unified EEG data preprocessing pipeline, it is convenient for researchers to share their research results, and experimental results are more easily reproduced. At the same time, beginners can quickly get started by reading the preprocessing code of others.

mne pandas scikit-learn numpy

You can refer to the implemented classes in paradigms folder to create preprocessing pipeline of your own. When you have written your own class, others only need the following lines of code to reproduce your experimental results.

from eegp.paradigms import MIFeetHand
from eegp.path import FilePath

class SaveData(MIFeetHand):
    def pipeline(self, filepaths):
        self.read_raw(filepaths)
        self.preprocess()
        self.make_epochs()
        self.make_data()
        self.save_data()

filepath = FilePath(subject='S1',
                     filetype='edf',
                     load_path='/tmp/data/S001R04.edf',
                     save_path='/tmp/data')
savedata = SaveData()
savedata.pipeline(filepaths)

This is an example that can be run online to let you know how easy it is to use when you share your pipeline with others.