【摘要】 提出基于Mahalanobis距离判别式算法的意识任务分类方法。对被测试者想象左右手运动时脑电信号的mu节律能量变化进行在线动态分析,提取EEG(C3,C4)两个通道的mu节律能量作为特征向量,用Mahalanobis距离判别式算法对左右手运动想象脑电模式进行分类,实验结果表明,正确识别率可达87.86%。更多还原

【Abstract】 The classification of metal activity based on Mahalanobis distance-based discriminant algorithm is proposed. The EEG signals are recorded during imagination of left and right hand movement. The mu rhythm energy of EEG and its online dynamic properties are analyzed. The mu rhythm of two channels (C3,C4) are extracted as feature vector,the event-related EEG patterns during left and right hand motor imagery are classified based on Mahalanobis distance-based discriminant algorithm. According to the analysis and experiment results,the correct rate of classification achieve 87.86 %.更多还原