MI-based BCI with accurate real-time three-class classification processing and light control application

计算机科学 支持向量机 人工智能 预处理器 脑-机接口 可靠性(半导体) 模式识别(心理学) 特征提取 脑电图 心理学 量子力学 精神科 物理 功率(物理)
作者
Jiakai Zhang,Boyang Xu,Xiongjie Lou,Yan Wu,Xiaoyan Shen
出处
期刊:Proceedings Of The Institution Of Mechanical Engineers, Part H: Journal Of Engineering In Medicine [SAGE Publishing]
卷期号:237 (8): 1017-1028 被引量:1
标识
DOI:10.1177/09544119231187287
摘要

The use of brain–computer interfaces (BCIs) to control intelligent devices is a current and future research direction. However, the challenges of low accuracy of real-time recognition and the need for multiple electroencephalographic channels are yet to be overcome. While a number of research teams have proposed many ways to improve offline classification accuracy, the potential problems in real-time experiments are often overlooked. In this study, we proposed a label-based channel diversion preprocessing to solve the problem of low real-time classification accuracy. The Tikhonov regularised common spatial-pattern algorithm (TRCSP) and one vs rest support vector machine (OVR-SVM) were used for feature extraction and pattern classification. High accuracy was achieved in real-time three-class classification using only three channels (average real-time accuracy of 87.46%, with a maximum of 90.33%). In addition, the stability and reliability of the system were verified through lighting control experiments in a real environment. Using the autonomy of MI and real-time feedback of light brightness, we have built a fully autonomous interactive system. The improvement in the real-time classification accuracy in this study is of great significance to the industrialisation of BCI.
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