Rashid Mamunur, Sulaiman Norizam, P P Abdul Majeed Anwar, Musa Rabiu Muazu, Ab Nasir Ahmad Fakhri, Bari Bifta Sama, Khatun Sabira
Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia.
Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia.
Front Neurorobot. 2020 Jun 3;14:25. doi: 10.3389/fnbot.2020.00025. eCollection 2020.
Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.
脑机接口（BCI）本质上旨在通过利用脑电波来控制不同的辅助设备。值得注意的是，BCI的应用并不局限于医学应用，因此，该领域的研究已受到应有的关注。此外，过去二十年中大量的相关出版物进一步表明了在这一特定领域所取得的持续改进和突破。尽管如此，同样值得一提的是，随着这些改进，新的挑战也在不断被发现。本文对完整BCI系统的最新技术进行了全面综述。首先，对基于脑电图（EEG）的BCI系统进行了简要概述。其次，从电生理控制信号、特征提取、分类算法和性能评估指标等方面对大量流行的BCI应用进行了综述。最后，讨论了当前BCI系统面临的挑战，并推荐了缓解这些问题的可能解决方案。
