MDD-Detection-with-EEG-Signals-using-a-Time-Series-Approach

This is a repository for our paper titled Automated Detection of Major Depressive Disorder with EEG Signals: A Time Series Classification Using Deep Learning published in IEEE Access This study focuses on the automated detection of MDD using EEG data and deep neural network architecture. For this aim, first, a customized InceptionTime model is recruited to detect MDD individuals via 19-channel raw EEG signals. Then, a channel-selection strategy, which comprises three channel-selection steps, is conducted to omit redundant channels.

The original InceptionTime paper also is available on here.

The proposed Inception network architecture

Data

The data used in this project comes from the MDD Patients and Healthy Controls EEG Data.

Requirements

You will need to install the following packages present in the requirements.txt file.

Code

The code is divided as follows:

The Inception classifier python file contains the Inception module python code using Keras library.
The Opening and sorting the files python folder contains the steps of opening and labeling the files.
The Channel selection python file involves general concepts of the channel selections approaches.

Reference

If you are interested in this work, please cite:

@ARTICLE{9828387,
  author={Rafiei, Alireza and Zahedifar, Rasoul and Sitaula, Chiranjibi and Marzbanrad, Faezeh},
  journal={IEEE Access}, 
  title={Automated Detection of Major Depressive Disorder With EEG Signals: A Time Series Classification Using Deep Learning}, 
  year={2022},
  volume={10},
  number={},
  pages={73804-73817},
  doi={10.1109/ACCESS.2022.3190502}}