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Sensors 2017, 17(6), 1385; doi:10.3390/s17061385

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

1
Department of Psychiatry, National Taiwan University Hospital, Taipei 10051, Taiwan
2
School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei 10051, Taiwan
3
Graduate Institute of Mechatronics Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
4
Department of Mechanical Engineering, Chung Yuan Christian University, Chungli 32023, Taiwan
5
Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
These authors contributed equally to this work
*
Author to whom correspondence should be addressed.
Academic Editor: Ioannis Kompatsiaris
Received: 14 April 2017 / Revised: 7 June 2017 / Accepted: 10 June 2017 / Published: 14 June 2017
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
View Full-Text   |   Download PDF [1404 KB, uploaded 15 June 2017]   |  

Abstract

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. View Full-Text
Keywords: major depressive disorder; electroencephalography (EEG); brain-computer interface (BCI); common spatial pattern (CSP); machine learning major depressive disorder; electroencephalography (EEG); brain-computer interface (BCI); common spatial pattern (CSP); machine learning
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MDPI and ACS Style

Liao, S.-C.; Wu, C.-T.; Huang, H.-C.; Cheng, W.-T.; Liu, Y.-H. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns. Sensors 2017, 17, 1385.

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