Real-time EEG-based emotion monitoring using stable features

In human-computer interaction (HCI), electroencephalogram (EEG) signals can be added as an additional input to computer. An integration of real-time EEG-based human emotion recognition algorithms in human-computer interfaces can make the users experience more complete, more engaging, less emotionally stressful or more stressful depending on the target of the applications. Currently, the most accurate EEG-based emotion recognition algorithms are subject-dependent, and a training session is needed for the user each time right before running the application. In this paper, we propose a novel real-time subject-dependent algorithm with the most stable features that gives a better accuracy than other available algorithms when it is crucial to have only one training session for the user and no re-training is allowed subsequently. The proposed algorithm is tested on an affective EEG database that contains five subjects. For each subject, four emotions (pleasant, happy, frightened and angry) are induced, and the affective EEG is recorded for two sessions per day in eight consecutive days. Testing results show that the novel algorithm can be used in real-time emotion recognition applications without re-training with the adequate accuracy. The proposed algorithm is integrated with real-time applications Emotional Avatar and Twin Girls to monitor the users emotions in real time.