Zhang Zhongjie, Koike Yasuharu
Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan.
Front Neurosci. 2022 Aug 16;16:867480. doi: 10.3389/fnins.2022.867480. eCollection 2022.
A technology that allows humans to interact with machines more directly and efficiently would be desirable. Research on brain-computer interfaces (BCIs) provides the possibility for computers to understand human thoughts in a straightforward manner thereby facilitating communication. As a branch of BCI research, motor imagery (MI) techniques analyze the brain signals and help people in many aspects such as rehabilitation, clinical applications, entertainment, and system controlling. In this study, an imagery experiment consisting of four kinds of right-hand movements (gripping, opening, pronation, and supination) was designed. Then a novel feature, namely, clustered feature was proposed based on the event-related spectral perturbation (ERSP) calculated from the EEG signal. Based on the selected features, two classical classifiers (support vector machine and linear discriminant classifier) were trained, achieving an acceptable accurate result (80%, on average).
一种能让人类更直接、高效地与机器交互的技术将是令人期待的。脑机接口（BCI）研究为计算机以直接的方式理解人类思维从而促进交流提供了可能性。作为BCI研究的一个分支，运动想象（MI）技术分析脑信号并在康复、临床应用、娱乐和系统控制等诸多方面帮助人们。在本研究中，设计了一个由四种右手动作（抓握、张开、旋前和旋后）组成的想象实验。然后基于从脑电图（EEG）信号计算出的事件相关谱扰动（ERSP）提出了一种新特征，即聚类特征。基于所选特征，训练了两种经典分类器（支持向量机和线性判别分类器），平均获得了可接受的准确结果（80%）。
