Vol.68, No.3, 2021, pp.3825-3840, doi:10.32604/cmc.2021.016893
OPEN ACCESS
ARTICLE
Enhanced Accuracy for Motor Imagery Detection Using Deep Learning for BCI
  • Ayesha Sarwar1, Kashif Javed1, Muhammad Jawad Khan1, Saddaf Rubab1, Oh-Young Song2,*, Usman Tariq3
1 National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan
2 Department of Software, Sejong University, Seoul, Gwangjin-gu, Korea
3 College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
* Corresponding Author: Oh-Young Song. Email:
(This article belongs to this Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Received 14 January 2021; Accepted 01 March 2021; Issue published 06 May 2021
Abstract
Brain-Computer Interface (BCI) is a system that provides a link between the brain of humans and the hardware directly. The recorded brain data is converted directly to the machine that can be used to control external devices. There are four major components of the BCI system: acquiring signals, preprocessing of acquired signals, features extraction, and classification. In traditional machine learning algorithms, the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data. The major reason for this is, features are selected manually, and we are not able to get those features that give higher accuracy results. In this study, motor imagery (MI) signals have been classified using different deep learning algorithms. We have explored two different methods: Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). We test the classification accuracy on two datasets: BCI competition III-dataset IIIa and BCI competition IV-dataset IIa. The outcome proved that deep learning algorithms provide greater accuracy results than traditional machine learning algorithms. Amongst the deep learning classifiers, LSTM outperforms the ANN and gives higher classification accuracy of 96.2%.
Keywords
Brain-computer interface; motor imagery; artificial neural network; long-short term memory; classification
Cite This Article
A. Sarwar, K. Javed, M. J. Khan, S. Rubab, O. Song et al., "Enhanced accuracy for motor imagery detection using deep learning for bci," Computers, Materials & Continua, vol. 68, no.3, pp. 3825–3840, 2021.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.