Description

Background: 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. Methods: We proposed an effective electroencephalogrambased 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. Results and discussion: We achieved a leaveone-subject-outcross-validationclassificationresult 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 evaluatedthe classificationperformance using individual frequency bands and found that the contribution of the gamma band was predominant. In addition, we evaluateddifferent classifiers,includingk-nearest neighbor and logistic regression, which showed similar trends in the improvement of classification by employing TCSP.