运动表象
脑电图
运动区
计算机科学
人工智能
运动(音乐)
脑-机接口
模式识别(心理学)
语音识别
心理学
神经科学
美学
哲学
作者
Kun Wang,Minpeng Xu,Shanshan Zhang,Yufeng Ke,Dong Ming
标识
DOI:10.1109/embc.2018.8512184
摘要
Motor imagery-based BCIs are the most natural human-computer interaction paradigms. In recent years, researchers have tried to decode the kinetic information of motor imagery. In this paper, we analyzed and discriminated the EEG patterns of different force levels motor imagery using MRCPs. In the experiment, nine healthy subjects were required to perform the hand force motor imagery tasks (30% MVC and 10% MVC). From the view of MRCPs, the most significant discrimination between the two levels of mental tasks was the manifestation of motor planning. The average classification accuracy for features involving both MRCP and CSP was 78.3%, which was 8.5% higher than the CSP-based features (p¡0.001) and 2% higher than the MRCP-based features. The results demonstrated the feasibility of using MRCPs for hand force motor imagery classification.
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