融合共空间模式与脑网络特征的EEG抑郁识别

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

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。

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 chan-nels is constructed according to the phase locking values,and the functional connection modes of two classes under differ-ent 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 fea-ture 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.