Classification model of arousal and valence mental states by EEG signals analysis and Brodmann correlations
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2015, Vol 6, Issue 6
Abstract
This paper proposes a methodology to perform emotional states classification by the analysis of EEG signals, wavelet decomposition and an electrode discrimination process, that associates electrodes of a 10/20 model to Brodmann regions and reduce computational burden. The classification process were performed by a Support Vector Machines Classification process, achieving a 81.46 percent of classification rate for a multi-class problem and the emotions modeling are based in an adjusted space from the Russell Arousal Valence Space and the Geneva model.
Authors and Affiliations
Adrian Aguinaga, Miguel Ramirez, Maria Flores
Keywords
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- EP ID EP137750
- DOI 10.14569/IJACSA.2015.060633
- Views 99
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