Stochastic Fluctuation in EEG Evaluated via Scale Mixture Model for Decoding Emotional Valence

Shunya Fukuda, Akira Furui, Maro G. Machizawa, Toshio Tsuji

Abstract: Electroencephalogram (EEG) analysis has garnered attention as a method for quantitatively decoding human emotions, and EEG amplitude values in specific frequency bands are typically used for this purpose. However, as brain states can fluctuate rapidly in response to external stimuli, accounting for temporal fluctuations in amplitude could enhance the accuracy of emotion decoding. In this paper, we investigate the relationship between pleasant/unpleasant emotions and fluctuations in EEG amplitude by utilizing a scale mixture model that assumes a hierarchical stochastic structure for EEG variance. This model focuses on the connection between the non- Gaussianity of the EEG amplitude distribution and stochastic fluctuation of the EEG variance (i.e., amplitude), which can be quantitatively evaluated by introducing a feature value. In the experiments, we used an EEG dataset obtained during the presentation of pleasant and unpleasant images and computed the proposed and conventional features, such as simple variance and approximate entropy values, for comparison. Statistical tests and receiver operating characteristic analyses of the calculated features indicated that the proposed feature, which reflects the stochastic fluctuation of variance, can distinguish between pleasant and unpleasant emotions more accurately than conventional features. These findings suggest that not only the conventional amplitude value but also its fluctuation, may be useful in assessing emotional valence.