Cross-subject classification of depression by using multiparadigm EEG feature fusion.

BACKGROUND AND OBJECTIVE

The aim of this study is to solve the non-stationarity and complexity characteristics and huge individual differences in the electroencephalogram (EEG) signals for depression classification.

METHODS

To address those problems, the Lempel-Ziv complexity feature matrices were extracted from the EEG signals under the two paradigms of eyes open and eyes closed in the resting state. Topographical map of brain and statistical analysis were introduced to investigate the significance of eyes open and eyes closed EEG for depression classification. To promote the classification accuracy, feature matrices from the two paradigms were fused. And linear combination and concatenation fusion methods were proposed to further reveal the underlying mechanism of improving classification accuracy. Support vector machine (SVM), K-nearest neighbor, and decision tree classifiers were employed and compared to classify depression under the eyes open, eyes closed and fused paradigm.

RESULTS

The classification results of 10-fold cross-validation showed that the highest average accuracy (86.58%) under a single paradigm was achieved in the eyes-open state. The multiparadigm fusion method of concatenation was better than the linear combination. The best classification result was obtained using multiparadigm feature concatenation under the SVM classifier, yielding an accuracy of 94.03%.

CONCLUSION

The multiparadigm feature fusion method proposed in this paper can effectively improve the accuracy of depression classification. It was proved that eyes open and eyes closed EEG have complementary information, which was benefit for the cross-subject classification of depression. It provides new ideas for depression classification in clinics.