Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Sensors (Basel). 2019 Mar 22;19(6):1423. doi: 10.3390/s19061423.
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.
基于脑电图（EEG）的脑机接口（BCI），特别是那些使用运动想象（MI）数据的接口，有潜力成为临床和娱乐领域的突破性技术。当主体想象肢体运动时，就会产生 MI 数据。本文回顾了基于 MI 的 EEG-BCI 的最新信号处理技术，特别关注了所使用的特征提取、特征选择和分类技术。它还总结了基于 EEG 的 BCI 的主要应用，特别是基于 MI 数据的应用，最后详细讨论了阻碍 EEG 基 BCI 发展和商业化的最普遍挑战。
