Stability of features in real-time EEG-based emotion recognition algorithm

Stability of algorithms is very important for electroencephalogram (EEG) based applications. Stable features should exhibit consistency among repeated measurements of the same subject. Previously, power features were reported to be one of the most stable EEG features in medical application. In this paper, stability of features in emotion recognition algorithms is studied. Our hypothesis is that the most stable features give the best intra-subject accuracy across different days in real-time emotion recognition algorithm. An experiment to induce 4 emotions such as pleasant, happy, frightened, and angry is designed and carried out in 8 consecutive days (two sessions per day) on 4 subjects to record EEG data. A novel real-time subject-dependent algorithm with the most stable features is proposed and implemented. The algorithm needs just one training for each subject. The training results can be used in real-time emotion recognition applications without re-training with the adequate accuracy. The proposed algorithm is integrated with a realtime application "Emotional Avatar"