Abstract:

Revealing human brain activity through machine learning EEG(Electroencephalogram) has become an important scheme to explore the inner emotional state of humans. Because the change of emotion state is dynamic rather than constant, it is difficult to predict the change of emotion state in the field of emotion recognition. This study proposes a label generation framework for instantaneous emotion intensity. A set of supervised labels is generated by having subjects watch videos that stimulate and capture their instantaneous emotional intensity, and combine the supervised labels with EEG features to generate three sets of semi-supervised labels to correspond to the instantaneous emotional state changes of subjects. In this study, EEG features and various machine learning methods are used to analyze the applicability of four groups of labels to emotional state changes. The support vector machine model achieves 80.02%, 54.76% and 56.14% classification accuracy for two-class, three-class and four-class sentiment intensities on supervised label sets. The experimental results show that the supervised instantaneous emotion intensity labels are more universal for EEG data and emotional state changes across different subjects.

Key words: machine learning, electroencephalogram, emotional state, emotion recognition, label generation, instantaneous emotion intensity label, universal label, subject specific labels