Individual-based Estimation of Valence with EEG

Abstract

In this study, it is determined individual-based features which are used to estimate emotional negative valence and compared the features effectiveness with different classifiers. Ten movie clips are shown to subjects as an emotional stimuli and EEG recording is recorded synchronously. Emotional valence value is scored in [-7 7] Likert scale by the subjects immediately after video ended. According to lowest and highest valence values, two classes are generated. The data is processed on an individual basis and personal spatial filters is obtained by Independent Component Analysis. After calculating the spectrogram of the spatial filtered data, features are extracted by subtracting amplitudes of 3Hz averaged frequency bands. The result of feature selection, it is observed that features from beta and gamma bands are much more effective. The success rate of the selected features was tested with five classifiers by cross validation, and high performance was obtained from multilayer perceptron classifiers and the instance-based k-nearest neighborhood algorithm (IBk-NN). The average accuracies of IBk-NN and multilayer classifier are achieved 86% +/- 8 and 83% +/- 9, respectively.