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作 者:杨丰玮 陈鹏[1] 郗凯 蒲华林 刘雪垠 Yang Fengwei;Chen Peng;Xi Kai;Pu Hualin;Liu Xueyin(School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China;Sichuan Provincial Machinery Research&Design Institute(Group)Co,Ltd,Chengdu 610041,China)
机构地区:[1]西南交通大学机械工程学院,四川成都610031 [2]四川省机械研究设计院(集团)有限公司,四川成都610041
出 处:《系统仿真学报》2023年第2期254-267,共14页Journal of System Simulation
基 金:四川省科技计划(2021ZHYZ0019)。
摘 要:基于脑电图EEG(electroencephalogram)的脑机接口BCI(brain computer interface)系统可以帮助肢体运动障碍患者进行日常生活和康复训练。由于EEG信号的信噪比低、个体差异大,使得脑电信号的特征提取和分类存在精度和效率不高的问题,进而影响了在线BCI系统的广泛应用。提出一种融合空间信息的CNN(convolution neural network)用于MI(motor imagery)脑电信号的在线分类,结合运动想象ERD/ERS(event related desynchronization/event related synchronization)现象的对侧效应,对通道重新排序后的MI-EEG分别进行横向和纵向卷积,充分利用了MI-EEG中的空间信息,完成MI-EEG信号的实时采集和分类。结果分析表明:该方法具有一定的实时性和有效性,为在线MI-BCI系统的实现提供了基础。EEG-based BCI system can help the daily life and rehabilitation training of limb movement disorders patients. Due to the low signal-to-noise ratio and large individual differences of EEG signals,the accuracy and efficiency of EEG feature extraction and classification are not high, which affects the wide application of online BCI system. A CNN with spatial information is proposed for the online classification of MI-EEG signals. The reordered MI-EEG is convolved horizontally and vertically respectively. With the contralateral effect of motor imagery ERD/ERS phenomenon, the spatial information in MI-EEG is fully utilized to achieve the real-time acquisition and classification of MI-EEG signals. Experimental results show that the proposed method is effectively performed in real time, which provide a basis for the implementation of online MI-BCI system.
分 类 号:TP391.9[自动化与计算机技术—计算机应用技术]
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