Abstract:
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. How ever, using physiological signals requires using feature extraction and selection meth ods. 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 recog nition 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 im proved 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.|Keywords : Emotion Recognition, Tensor, Source Space, Sensor Space, Tensor Logis tic Regression, Support Vector Machine, EEG .
