基于HCSP和模糊熵的脑电信号分类

Classification of EEG signal based on HCSP and fuzzy entropy

针对共同空间模式特征提取算法(CSP)不能对频域信息进行处理,且在导联数较少的情况下应用效果不佳的问题,提出将希尔伯特-黄变换(HHT)与CSP相结合的算法。在原始脑电信号经过经验模态分解(EMD)后,提取每个导联的前三阶固有模态函数(IMF)及其组合重构信号,利用CSP特征提取,获取2维特征,联合计算信号的自回归模型参数(AR)和模糊熵组成融合特征向量,采用线性判别分类器对提取的特征进行分类。对第二届BCI竞赛提供的数据使用该方法进行特征提取,训练集和测试集分类准确率分别达到90%、88.6%,验证了该算法可有效改善运动想象辨识效果。

The common spatial pattern feature extraction algorithm(CSP)can not deal with the frequency domain information and the poor effects of application exist in the case of a small number of leads,a method combining Hilbert-Huang transform(HHT)with CSP was proposed.The first three intrinsic mode functions(IMF)and the reconstructed signal of each lead were extracted after the empirical mode decomposition(EMD)of the original electroencephalogram(EEG).CSP was used to obtain the two-dimensional feature vector,the autoregressive model parameters(AR)and fuzzy entropy of the EEG were calculated to constitute the feature vectors.The extracted features were classified.The data provided by the second BCI competition and the proposed method for feature extraction were used.Results of experiments show the classification accuracies of 90%and 88.6%are achieved for the training set and test set.The method can affect the motion recognition greatly.