@article{ajcrr2014243,
author={Sorkhabi, Majid Memarian},
title={Emotion Detection from EEG signals with Continuous Wavelet Analyzing},
journal={American Journal of Computing Research Repository},
volume={2},
number={4},
pages={66--70},
year={2014},
url={http://pubs.sciepub.com/ajcrr/2/4/3},
abstract={Recently, the field of Brain-Computer interface has gained a great deal of attention. In this work, we present some promising results of our research in classification of emotions induced by watching music videos. More specially, we aim to analyze users' passive physiological responses as they watch video clips. We use DEAP data base for this purpose. We show robust correlations between usersĄŻ self-assessments of arousal and valence and the frequency Entropy and powers of their EEG activity. Also we found that high frequency bands give higher accuracy than low frequency bands especially EEG in Gamma band that give accuracy at 73.84% (for valence) and 69.82% (for arousal). EEG signals were decomposed to 5 frequency bands by Continuous Wavelet Transform (CWT) using the 2.8 Biorthogonal wavelet.},
doi={10.12691/ajcrr-2-4-3}
publisher={Science and Education Publishing}
}