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.

Highlights

  • Major depressive disorder (MDD) is a prevalent mood disorder with the presentation of persistent sadness, feeling of worthlessness, restlessness, and loss of interest in daily activities [1], and patients may suffer problems in decision making and concentration [2]

  • The results show that the proposed KEFB-common spatial pattern (CSP) feature is superior to the existing features

  • The results reveal reveal that both support vector machine (SVM) and k-nearest neighbor (k-NN) classifiers achieved their best performance with small numbers that both and k-NN

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Summary

Introduction

Major depressive disorder (MDD) is a prevalent mood disorder with the presentation of persistent sadness, feeling of worthlessness, restlessness, and loss of interest in daily activities [1], and patients may suffer problems in decision making and concentration [2]. Despite the high impact on human health, the overall remission rates of treatments with either psychopharmacological or psychotherapeutic therapies for MDD are modest [5,6]. One possible reason for sub-optimal treatment response is the heterogeneous etiology of MDD, which is a phenomena based rather than a laboratory-test based diagnosis [1]. Biomarkers that include neuroimage, genetic fingerprint, proteomics, neuroendocrine function tests, and electrophysiological measurements, may help elucidate the endophenotypic signs for refining diagnosis and even unraveling the whole picture of MDD [7,8]

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