EEG-Based Identification of Latent Emotional Disorder Using the Machine Learning Approach

Emotion influences our daily life to a large extent, especially for those who are undergoing bad mood and have high risk for emotional disorders. It is hard to recognize them, but very important so that we can provide intervention before them getting worse. This study used EEG signals to recognize who has high risk for emotional disorders instead of emotion type only. The proposed machine learning method combined the features of multiple cortex areas and frequency bands to find the high risky group for emotional disorders through a kernel SVM classifier. It achieved the accuracy of 95.20%, with all cortex areas and all frequency bands. Results showed that the frontal cortex, central cortex and temporal cortex have a primary influence on identifying emotional disorder and can be used for the reference information for professional diagnose.