Emotion recognition of EEG data using tensor logistic regression

Emotion recognition is a research area gaining momentum in the last three decades with a strong impact on our daily life. One of the most widely used methods to study emotion recognition is using physiological signals such as EEG data. However, using physiological signals requires using feature extraction and selection methods. Moreover, there is no gold standard for choosing the best methods. Therefore, this study aims to compare the sensor space and source space EEG data for emotion recognition using tensor based methods. In order to achieve that, different frequency bands were used as features of EEG data. In addition, support vector machine (SVM) as a conventional method, and logistic tensor regression (LTR), which was a tensor-based method, were used as two different classification methods. The results showed that the gamma was the most discriminating frequency band. Also, source space data improved the accuracy rates when compared with sensor space data. Moreover, TLR was superior in the source space than SVM. In the sensor space, both methods performed similarly.