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.

Adrian Rodriguez Aguinaga, Miguel Angel Lopez Ramirez and Maria del Rosario Baltazar Flores, “Classification model of arousal and valence mental states by EEG signals analysis and Brodmann correlations” International Journal of Advanced Computer Science and Applications(IJACSA), 6(6), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060633

@article{Aguinaga2015,
title = {Classification model of arousal and valence mental states by EEG signals analysis and Brodmann correlations},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060633},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060633},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {6},
author = {Adrian Rodriguez Aguinaga and Miguel Angel Lopez Ramirez and Maria del Rosario Baltazar Flores}
}