【摘要】 眼电伪迹和噪声是导致脑电信号低信噪比的重要原因,会降低运动想象任务的分类性能。提出一种改进的基于少通道数的分块欠定盲源分离的滤波方法,通过分块的思想把非平稳的脑电信号变为近平稳的分块信号,利用二阶欠定混合矩阵盲识别方法估计混合分离矩阵,然后通过基于最小均方误差的波速形成器提取源信号,接着通过得分准则自动去除噪声信号并重构信号,最后提取共空间模式特征进行分类。想象运动的真实脑电信号实验仿真结果表明,分块欠定盲源分离方法能很好地恢复源信号并能有效地去除眼电等伪迹和噪声,共空间模式特征则提高了想象任务识别率。更多还原

【Abstract】 One of the key problems of brain-computer interfaces(BCI)is low signal-to-noise ratio(SNR)of electroencephalogram(EEG)signals.It affects recognition performance.To remove the artifact and noise,block under-determined blind source separation method based on the small number of channels is proposed in this paper.The nonstationary EEG signals are turned into block stationary signals by piecewise.The mixing matrix is estimated by the second-order under-determined blind mixing matrix identification.Then,the beamformer based on minimum mean square error separates the original sources of signals.Eventually,the reconstructed EEG for mixed signals removes the unwanted components of source signals to achieve suppressing artifact.The experiment results on the real motor imagery BCI indicated that the block under-determined blind source separation method could reconstruct signals and remove artifact effectively.The accuracy of motor imagery task of BCI has been greatly improved.更多还原