Abstract
We introduce a system called AMBER (Advanced Multi-modal Biometric Emotion Recognition), which combines Electroencephalography (EEG) with Electro Dermal Activity (EDA) and pulse sensors to provide low cost, portable real-time emotion recognition. A single-subject pilot experiment was carried out to evaluate the ability of the system to distinguish between positive and negative states of mind provoked by audio stimuli. Eight single classifiers and six ensemble classifiers were compared using Weka. All ensemble classifiers outperformed the single classifiers, with Bagging, Rotation Forest and Random Subspace showing the highest overall accuracy.
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Kuncheva, L.I., Christy, T., Pierce, I., Mansoor, S.P. (2011). Multi-modal Biometric Emotion Recognition Using Classifier Ensembles. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_32
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DOI: https://doi.org/10.1007/978-3-642-21822-4_32
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