Multi-class Motor Imagery EEG Classification using Convolution Neural Network

Amira Echtioui, Amira Echtioui, Wassim Zouch, Mohamed Ghorbel, Chokri Mhiri, Chokri Mhiri, Habib Hamam

2021

Abstract

Electroencephalogram (EEG) signals based on Motor Imagery (MI) are a widely used form of input in Brain Computer Interface (BCI). Although there are several ways to classify data, a question remains as to which method to use in EEG signals based on motor imagery. This article presents an attempt to reach the best classification method based on deep learning methods by comparing two models: Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), on the same basic data set. The BCI Competition IV dataset 2a was used as the base dataset to test the two classification methods. Experimental results show that the proposed CNN model outperforms the LSTM model, with an accuracy value of 74%, and other state-of-the-art methods.

Paper Citation

in Harvard Style

Echtioui A., Zouch W., Ghorbel M., Mhiri C. and Hamam H. (2021). Multi-class Motor Imagery EEG Classification using Convolution Neural Network. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: SDMIS, ISBN 978-989-758-484-8, pages 591-595. DOI: 10.5220/0010425905910595

in Bibtex Style

@conference{sdmis21,
author={Amira Echtioui and Wassim Zouch and Mohamed Ghorbel and Chokri Mhiri and Habib Hamam},
title={Multi-class Motor Imagery EEG Classification using Convolution Neural Network},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: SDMIS,},
year={2021},
pages={591-595},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010425905910595},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: SDMIS,
TI - Multi-class Motor Imagery EEG Classification using Convolution Neural Network
SN - 978-989-758-484-8
AU - Echtioui A.
AU - Zouch W.
AU - Ghorbel M.
AU - Mhiri C.
AU - Hamam H.
PY - 2021
SP - 591
EP - 595
DO - 10.5220/0010425905910595