多特征融合的运动想象脑电特征提取方法

针对单一特征识别率低、自适应性差等问题，提出一种基于希尔伯特-黄变换（HHT）和共同空间模式（CSP）的特征提取方法HCHT。首先，对原始脑电信号（EEG）进行经验模态分解（EMD）得到固有模态函数（IMF），并将IMF分量合并成新的信号矩阵；然后，对IMF进行希尔伯特谱分析，得到信号的时-频域特征；接着，对构造的信号矩阵进行进一步的CSP分解，将时-频域特征扩展成时-频-空域特征；最后，通过支持向量机（SVM）对特征集进行分类。在BCI Competition II数据集的实验表明，与HHT时-频域和CSP空域特征的方法相比，所提方法的识别准确率分别提高了7.5、10.3和9.2个百分点，且标准差更小。在智能轮椅平台进行在线实验的结果表明，HCHT能有效提高识别准确率和稳定性。

Motor imagery EEG feature extraction method based on multi-feature fusion

To solve the problems of low recognition rate and poor adaptability of single feature， a feature extraction method named Hilbert-CSP-Huang Transform （HCHT） was proposed based on Hilbert-Huang Transform （HHT） and Common Spatial Pattern （CSP）. Firstly， the Intrinsic Mode Function （IMF） was obtained by the Empirical Mode Decomposition （EMD） of original ElectroEncephaloGram （EEG） signals， and the IMF components were merged into a new signal matrix. Secondly， the time-frequency domain features were obtained by Hilbert spectrum analysis. Thirdly， the time-frequency domain features were extended into time-frequency-space features by further CSP decomposition of the constructed signal matrix. Finally， the feature set was classified by Support Vector Machine （SVM）. Experiments on the BCI Competition II dataset show that compared with methods based on HHT time-frequency and CSP spatial domain features， the proposed method has the recognition accuracy increased by 7.5， 10.3 and 9.2 percentage points respectively with smaller standard deviation. The online experimental results on the intelligent wheelchair platform show that HCHT can effectively improve the recognition accuracy and robustness.