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Towards emotion recognition from electroencephalographic signals

During the last decades, information about the emotional state of users has become more and more important in human-computer interaction. Automatic emotion recognition enables the computer to recognize a user's emotional state and thus allows for appropriate reaction, which may pave the way for computers to act emotionally in the future. In the current study, we investigate different feature sets to build an emotion recognition system from electroencephalo-graphic signals. We used pictures from the International Affective Picture System to induce three emotional states: pleasant, neutral, and unpleasant. We designed a headband with four build-in electrodes at the forehead, which was used to record data from five subjects. Compared to standard EEG-caps, the headband is comfortable to wear and easy to attach, which makes it more suitable for everyday life conditions. To solve the recognition task we developed a system based on support vector machines. With this system we were able to achieve an average recognition rate up to 66.7% on subject dependent recognition, solely based on EEG signals.