脑机接口在线识别左右手运动想象的脑电信号分析(英文)

Electroencephalogram recognition of imaginary right and left hand movements by brain-computer interface

采用脑机接口2003竞赛中Graz科技大学提供的脑电数据,用小波包分解获取8～16 Hz 脑电信号,计算 C3,C4 电极脑电信号的功率谱峰值和对应频率作为组合脑电特征向量,运用时变线性分类算法对运动意识任务运行分类。对 140 次实验的测试样本数据分析,最大分类正确率可达 89.29%,最大互信息和信噪比分别为0.622 8bit 和1.3713。提示C3,C4 电极 8～16 Hz 脑电信号功率谱峰值和对应的频率能很好地反映左右手运动想象脑电特征的变化,与事件相关去同步/事件相关同步现象变化一致,可在线识别左右手想象运动。

Based on the peak value of power spectral density （PPSD） and corresponding frequency （CF）, an approach that performs electroencephalogram （EEG） feature extraction during imaginary right and left hand movements was proposed. The data were gained from brain computer interface competition in 2003 provided by Graz University of Technology. The EEG signals between 8 -16 Hz were decomposed by db3 wavelet packet at three levels. The PPSD and CF of electrodes C3 and C4 were defined as the EEG feature vectors and calculated respectively. The left and right hand motor imaginary tasks were distinguished by the time-variable linear classifier. The proposed method was applied to the test data for 140 trials. The satisfactory results were obtained with the highest classification accuracy 89.29%. The maximum mutual information was 0.622 8 bit, and the signal-to-noise ratio （SNR） was 1.371 3. The PPSD and its CF on electrodes C3 and C4 between 8 and 16 Hz were coincident with event-related desynchronization （ERD） and event-related synchronization （ERS）. This method is simple, quick, and promising for on-line brain computer interface system.