Classification of EEG for Affect Recognition: An Adaptive Approach

Authors:
Omar Alzoubi;Rafael A. Calvo;Ronald H. Stevens
Affiliations:
School of Electrical and Information Engineering, The University of Sydney, Australia;School of Electrical and Information Engineering, The University of Sydney, Australia;IMMEX Project, University of California Los Angeles,
Venue:
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Year:
2009

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Abstract

Research on affective computing is growing rapidly and new applications are being developed more frequently. They use information about the affective/mental states of users to adapt their interfaces or add new functionalities. Face activity, voice, text physiology and other information about the user are used as input to affect recognition modules, which are built as classification algorithms. Brain EEG signals have rarely been used to build such classifiers due to the lack of a clear theoretical framework. We present here an evaluation of three different classification techniques and their adaptive variations of a 10-class emotion recognition experiment. Our results show that affect recognition from EEG signals might be possible and an adaptive algorithm improves the performance of the classification task.