Authors: Zirui Lan Olga Sourina Lipo Wang Yisi Liu
Publish Date: 2015/11/26
Volume: 32, Issue: 3, Pages: 347-358
Abstract
In human–computer interaction HCI electroencephalogram EEG signals can be added as an additional input to computer An integration of realtime EEGbased 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 EEGbased emotion recognition algorithms are subjectdependent and a training session is needed for the user each time right before running the application In this paper we propose a novel realtime subjectdependent 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 retraining 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 realtime emotion recognition applications without retraining with the adequate accuracy The proposed algorithm is integrated with realtime applications “Emotional Avatar” and “Twin Girls” to monitor the users emotions in real time
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