Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 6; p. 1385
• Main Authors: Liao, Shih-Cheng, Wu, Chien-Te, Huang, Hao-Chuan, Cheng, Wei-Teng, Liu, Yi-Hung
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14.06.2017; MDPI
• Subjects: Accuracy; Algorithms; brain-computer interface (BCI); Brain-Computer Interfaces; Classification; common spatial pattern (CSP); Depressive Disorder, Major; Electrodes; Electroencephalography; electroencephalography (EEG); Frequencies; Human-computer interface; Humans; machine learning; major depressive disorder; Mental depression; Patients; Principal components analysis; Signal Processing, Computer-Assisted; Support Vector Machine; Support vector machines; electroencephalography (EEG); major depressive disorder; machine learning; brain-computer interface (BCI); common spatial pattern (CSP)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17061385

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.