In this paper, we review different classification methods for emotion recognition from EEG and perform a detailed comparison of these methods on a relatively larger dataset of 45 experiments. We propose to combine the classifiers using stacking to improve the emotion recognition accuracies. Experimental results show that the combination of classifiers using stacking can achieve higher average accuracies than that without stacking methods. The weights derived from the classifiers are investigated to extract the relevant features and present their biological interpretation as critical brain areas and critical frequency bands.
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© 2015 Springer International Publishing Switzerland
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Zheng, WL., Santana, R., Lu, BL. (2015). Comparison of Classification Methods for EEG-based Emotion Recognition. In: Jaffray, D. (eds) World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada. IFMBE Proceedings, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-319-19387-8_287
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19386-1
Online ISBN: 978-3-319-19387-8