EEG Feature Extraction Based on Brain Function Network and Sample Entropy
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摘要:
针对脑-机接口(BCI)研究中采用单一特征对运动想象脑电信号(EEG)识别率不高的问题，该文提出一种结合脑功能网络和样本熵的特征提取方法。根据事件相关同步/去同步(ERS/ERD)现象以及皮层与肢体运动想象间的对侧映射机制，选取小波包变换消噪重构后的
\begin{document}$ \mu$\end{document} 节律脑电信号，用左侧27个通道、右侧27个通道分别对左半球脑区和右半球脑区构建脑功能网络，计算网络的平均节点度和平均聚集系数作为运动想象的脑功能网络特征，并结合C3, C4通道
节律的样本熵构筑分布性和指向性相结合的特征向量。选用支持向量机(SVM)对左右手运动想象脑电信号进行分类，结果表明基于脑功能网络和样本熵的特征提取方法能够实现更优的分类效果，分类准确率最高可达90.27%。
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关键词:
- 脑电信号 /
- 脑功能网络 /
- 样本熵 /
- 特征提取 /
- 事件相关同步/去同步
Abstract:For the low recognition rate of motor imagery ElectroEncephaloGram (EEG) signals using single feature in Brain-Computer Interface (BCI) research, a feature extraction method combining brain function network and sample entropy is proposed. According to the neural mechanism appearing in Event Related Synchronization/Event Related Desynchronization (ERS/ERD) phenomenon and the contralateral mapping mechanism between cortex and limb motor imagery, the μ rhythm is denoised by wavelet packet transform. The brain function network is constructed for left hemispherical brain region and right hemispherical brain region by μ rhythm of 27 left channels and 27 right channels respectively. The mean node degree and the mean clustering coefficient are calculated as the brain function network characteristics, and the feature vectors combining the distribution and directivity are constructed by the sample entropy of C3 and C4 channels with the μ rhythm. The Support Vector Machine (SVM) is used to classify the left hand and right hand motor imagery EEG signals. The results show that the feature extraction method based on brain function network and sample entropy achieves better classification result, and the highest classification rate reached 90.27%.
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表 1 不同特征向量平均正确识别率与方差(%)
特征向量 受试B 受试C 受试D 受试E 受试G 平均正确识别率 脑功能网络 75.37±1.02 79.58±1.54 80.71±1.90 74.42±1.39 82.32±0.58 78.48±7.89 样本熵 70.28±1.93 76.70±2.25 74.59±3.96 69.43±1.87 77.36±1.58 73.67±11.20 脑功能网络+样本熵 85.28±0.85 89.59±0.94 87.11±1.69 83.88±0.75 90.27±0.34 87.23±5.94 
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表 2 各受试不同特征t检验的p值
受试 (脑网络+样本熵，脑网络) (脑网络+样本熵，样本熵) B <0.01 <0.01 C <0.01 <0.01 D <0.01 <0.01 E <0.01 <0.01 G <0.01 <0.01
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