融合共空间模式与脑网络特征的EEG抑郁识别

提出共空间模式算法和脑网络拓扑属性融合的脑电信号（electroencephalography，EEG）特征，结合深度学习模型时序卷积网络（temporal convolution network，TCN）对抑郁组和对照组进行分类。根据相位锁值构建电极通道间相位同步性功能网络，分析不同频段下两种类别的功能连接模式。采用多特征融合方法将共空间模式特征和脑网络拓扑特征结合起来，最后结合Fisher score特征选择方法和分类器依赖结构，得到低维高效的特征子集并应用TCN进行分类。在抑郁数据集上的实验结果验证了所提策略的有效性。

EEG Depression Recognition Based on Feature Fusion of Common Spatial Pattern and Brain Connectivity

This paper extracts the features of electroencephalography（EEG） signals based on common spatial pattern and brain connectivity. The depression group and the control group are recognized by using a deep learning model temporal convolution network（TCN） based on these EEG features. The phase synchronization functional network between channels is constructed according to the phase locking values, and the functional connection modes of two classes under different frequency bands are also analyzed. To contain more comprehensive information, the features between common spatial pattern and brain connectivity are combined. Finally, the Fisher score and classifier dependent structure are used for feature selection. As a result, these low-dimensional and efficient features are fed into a TCN classifier to detect depression. Experimental results on a depression dataset validate the effectiveness of the proposed strategy.