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 Song^2,*, 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 the Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)

Computers, Materials & Continua 2021, 68(3), 3825-3840. https://doi.org/10.32604/cmc.2021.016893

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%.

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Keywords

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