Exploring the Intrinsic Features of EEG signals via Empirical Mode Decomposition for Depression Recognition

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2023-01, Vol.31, p.1-1
• Main Authors: Shen, Jian, Zhang, Yanan, Liang, Huajian, Zhao, Zeguang, Dong, Qunxi, Qian, Kun, Zhang, Xiaowei, Hu, Bin
• Format: Article
• Language: English
• Subjects: Brain; Cognitive ability; Computational neuroscience; Data mining; Decomposition; Depression; Depression - diagnosis; Depression recognition; EEG; Electroencephalography; Electroencephalography - methods; Empirical mode decomposition; Entropy; Feature extraction; Humans; intrinsic features; Mental depression; Parameters; Physiology; Recognition, Psychology; Regularization; Signal Processing, Computer-Assisted
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2022.3221962

Depression is a severe psychiatric illness that causes emotional and cognitive impairment and has a considerable impact on patients' thoughts, behaviors, feelings and well-being. Moreover, methods for recognizing and treating depression are lacking in clinical practice. Electroencephalogram (EEG) signals, which objectively reflect the internal workings of the brain, is a promising and objective tool for recognizing and diagnosing of depression and enhancing clinical effects. However, previous EEG feature extraction methods have not performed well when exploring the intrinsic characteristics of highly complex and non-stationary EEG signals. To address this issue, we propose a regularization parameter-based improved intrinsic feature extraction method of EEG signals via empirical mode decomposition (EMD), which mines the intrinsic patterns in EEG signals, for depression recognition. Furthermore, our method can effectively solve the problem that EMD fails to extract intrinsic features. In this method, we first select an appropriate regularization parameter to generate the regularization matrix. Next, we calculate the sum of the matrix products of the IMFs and the regularization matrix and leverage the inverse of this matrix to extract the intrinsic features. The classification results of our method on four EEG datasets reached 0.8750, 0.8850, 0.8485 and 0.7768, respectively. In addition, compared with the iEMD method, our method requires less computational costs. These results support our claim that our method can effectively strengthen the depression recognition performance, and our method outperforms state-of-the-art feature extraction approaches.