Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.
针对运动想象脑电信号因受试者个体差异导致辨识精度低的问题，本文提出了基于个体自适应的运动想象脑电信号特征表征方法。首先从个体差异和频带信号特点出发，提出了基于拓展式相关特征（ReliefF）的自适应通道选择方法；通过提取各频带信号5个时频域观察特征，运用ReliefF算法对各频带信号通道进行有效性评估，进而实现各频带信号通道选择。其次提出了基于快速相关滤波算法（FCBF）的共空间模式（CSP）特征表征方法（CSP-FCBF）；通过CSP提取脑电信号特征，运用FCBF进行特征优化得到最优特征集，从而实现运动想象脑电信号状态有效表征。最后使用支持向量机（SVM）作为分类器进行分类辨识。实验分析结果表明，本文所提方法能有效实现运动想象脑电信号状态表征，四类状态平均辨识精度达到了（83.0±5.5）%，较传统的CSP特征表征方法提高6.6%。本文在运动想象脑电信号特征表征方面取得的研究成果，为实现自适应的脑电信号解码及其应用奠定了基础。.
Keywords: Brain-computer interface; Channel selection; Feature representation; Individual adaptation; Motor imagery electroencephalogram.