Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (22): 150-158.DOI: 10.3778/j.issn.1002-8331.2104-0329

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

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

WANG Yixin, ZHU Xiangru, YANG Lijun   

  1. 1.School of Mathematics and Statistics, Henan University, Kaifeng, Henan 475004, China
    2.Institute of Cognition, Brain and Health, Henan University, Kaifeng, Henan 475004, China
    3.Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Kaifeng, Henan 475004, China
  • Online:2022-11-15 Published:2022-11-15



  1. 1.河南大学 数学与统计学院,河南 开封 475004
    2.河南大学 认知、脑与健康研究所,河南 开封 475004
    3.河南省人工智能理论及算法工程研究中心,河南 开封 475004

Abstract: 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.

Key words: depression recognition, electroencephalography(EEG), common spatial pattern, temporal convolution network(TCN), feature selection

摘要: 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。

关键词: 抑郁识别, 脑电信号(EEG), 共空间模式, 时序卷积网络(TCN), 特征选择