Hossain Khondoker Murad, Islam Md Ariful, Hossain Shahera, Nijholt Anton, Ahad Md Atiqur Rahman
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, United States.
Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh.
Front Comput Neurosci. 2023 Jan 16;16:1006763. doi: 10.3389/fncom.2022.1006763. eCollection 2022.
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.
在过去十年中，中枢神经系统生物信息学和计算创新方面的突破推动了脑机接口（BCI）的重大发展，使其成为应用科学和研究的前沿领域。BCI的复兴为身体残疾患者（如瘫痪患者和偏瘫患者）以及脑损伤患者（如中风患者）带来了神经康复策略。针对基于脑电图（EEG）的BCI应用，已经开发出了不同的方法。由于缺乏大量的EEG数据，使用矩阵分解和机器学习的方法最为流行。然而，最近情况发生了变化，因为现在有许多大型、高质量的EEG数据集被公开，并用于基于深度学习的BCI应用中。另一方面，深度学习在利用EEG数据解决诸如运动想象分类、癫痫发作检测和驾驶员注意力识别等复杂相关任务方面展现出了巨大的前景。目前，研究人员在BCI领域针对基于深度学习的方法开展了大量工作。此外，对于一项仅强调基于EEG的BCI应用深度学习模型的研究有着巨大需求。因此，我们将本研究介绍给近期提出的使用EEG数据（2017年至2022年）的基于深度学习的BCI方法。文中介绍了这些方法的主要差异，如优点、缺点和应用。此外，我们指出了当前面临的挑战以及未来研究的方向。我们认为这篇综述研究将有助于EEG研究群体开展未来的研究。
