A Spectro-Statistical Approach for Emotion Identification from EEG Signals

Automatic identification of emotions is important in human-centered computing. It allows machines to better understand user emotions. Identifying emotions via neural sensing techniques such as electroencephalogram (EEG) is a promising approach. In this paper, we aim to identify the emotions class from EEG signals. We frame emotion identification as a classification task and apply spectral and statistical encoders to extract the relevant features. We validate our approach on EmoNeuroDB dataset. Our method outperforms the EmoNeuroDB baseline, achieving a 42.10% increase in class prediction accuracy.