Computational model of affective states profiling using commercial 14-channel wireless EEG - Yaacob, Hamwira Sakti; Abdul Rahman, Abdul Wahab (2015)
Many studies apply computational models for affective states profiling through brain activities manifestations which are captured using electroencephalogram (EEG) devices. Although various kinds of devices are available for research purposes, such products are not available off-the-shelf. Moreover, most of EEG devices refrain users from certain movements. This becomes a challenge for capturing EEG signals during active and vibrant tasks. Thus, the aim of this study is to explore the potential of using commercial 14-channel wireless EEG for capturing the brain signals and affective states profiling through computational approach. In this study, power spectral density (PSD) is used as features. Different approaches of feature extractions are compared including the average performance of affective state classification using exclusively alpha and beta frequency bands, the average of energy density over alpha through beta frequency bands and the combination of alpha and beta power spectral density. Multilayer perceptron (MLP) neural networks are used for classification of affective states based on valence and arousal. Based on 11-fold cross validation, the classification of affective states using spectral features containing alpha and beta frequency bands produced accuracy above 80 %. In short, results have shown that 14-channel EPOC neuroheadset is viable for performing affective states profiling.