Toward EEG-Based Objective Assessment of Emotion Intensity.

Understanding the temporal dynamics of emotion poses a significant challenge due to the lack of methods to measure them objectively. In this study, we propose a novel approach to tracking intensity (EI) based on electroencephalogram (EEG) during continuous exposure to affective stimulation. We design selective sampling strategies to validate the association between the prediction outcome of an EEG-based emotion recognition model and the prominence of emotion-related EEG patterns, evidenced by the improvement in the classification task of discriminating arousal and valence by 2.01% and 1.71%, respectively. This study constitutes a breakthrough in the objective evaluation of the temporal dynamics of emotions, proposing a promising avenue to refine EEG-based emotion recognition models through intensity-selective sampling. Furthermore, our findings can contribute to future affective studies by providing a reliable and objective measurement method to profile emotion dynamics.