Status of deep learning for EEG-based brain-computer interface applications

Khondoker Murad Hossain; Md Ariful Islam; Shahera Hossain; Anton Nijholt; Md Atiqur Rahman Ahad

doi:10.3389/fncom.2022.1006763

Status of deep learning for EEG-based brain-computer interface applications

Front Comput Neurosci. 2023 Jan 16:16:1006763. doi: 10.3389/fncom.2022.1006763. eCollection 2022.

Authors

Khondoker Murad Hossain¹, Md Ariful Islam², Shahera Hossain³, Anton Nijholt⁴, Md Atiqur Rahman Ahad⁵

Affiliations

¹ Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, United States.
² Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh.
³ Kyushu Institute of Technology, Kitakyushu, Japan.
⁴ Human Media Interaction, University of Twente, Enschede, Netherlands.
⁵ Department of Computer Science and Digital Technology, University of East London, London, United Kingdom.

Abstract

In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain-computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.

Keywords: BCI; EEG; convolutional neural network (CNN); deep learning; future challenge.

Publication types

Review