Abstract
Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiological signals from the healthy controls when they are exposed to positive and negative stimuli. We proposed an effective electroencephalogram-based detection method for depression classification using spatial information. A face-in-the-crowd task, including positive and negative emotional facial expressions, was presented to 30 participants, including 16 depression patients and 14 healthy controls. Differential entropy and the genetic algorithm were used for feature extraction and selection, and a support vector machine was used for classification. A task-related common spatial pattern (TCSP) was proposed to enhance the spatial differences before the feature extraction. We achieved a leave-one-subject-out cross-validation classification result of 84% and 85.7% for positive and negative stimuli, respectively, using TCSP, which is statistically significantly higher than 81.7% and 83.2%, respectively, acquired without the TCSP (p < 0.05). We also evaluated the classification performance using individual frequency bands and found that the contribution of the gamma band was predominant. In addition, we evaluated different classifiers, including k-nearest neighbor and logistic regression, which showed similar trends in the improvement of classification by employing TCSP. The results show that our proposed method, employing spatial information, significantly improves the accuracy of classifying depression patients.
Highlights
D EPRESSION, as a common illness worldwide, is classified as a mood disorder and described as feelings of sadness or anger that interfere with a person’s everyday activities [1]
This focus was chosen because many studies have shown that subjects with depression exhibit different spatial responses in neurophysiological signals compared to healthy controls, when they are exposed to stimuli [8,9,10,11]
To observe the performance improvement with the task-related common spatial pattern (TCSP), we utilized three classification strategies: (a) a traditional method using all channels without feature selection; (b) a typical method using feature selection without employing the TCSP, where we used a genetic algorithm (GA); and (c) our proposed method using a GA with the TCSP
Summary
D EPRESSION, as a common illness worldwide, is classified as a mood disorder and described as feelings of sadness or anger that interfere with a person’s everyday activities [1]. Depression disorder is a pathological process that causes many symptoms, resulting in limited mental and physical functionality [3]. It is often accompanied by cognitive impairments, which may increase the risk of Alzheimer’s disease and suicide and accelerate cognitive decline [4]. This paper is focused on the experimental paradigm, emotion feature extraction, feature selection, machine learning, and the dataset for training and testing, on spatial information feature extraction and selection This focus was chosen because many studies have shown that subjects with depression exhibit different spatial responses in neurophysiological signals compared to healthy controls, when they are exposed to stimuli [8,9,10,11]
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