Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

This article presents an overview of the author's recent electrophysiological studies of anterior cerebral asymmetries related to emotion and affective style. A theoretical account is provided of the role of the two hemispheres in emotional processing. This account assigns a major role in approach- and withdrawal-related behavior to the left and right frontal and anterior temporal regions of two hemispheres, respectively. Individual differences in approach- and withdrawal-related emotional reactivity and temperament are associated with stable differences in baseline measures of activation asymmetry in these anterior regions. Phasic state changes in emotion result in shifts in anterior activation asymmetry which are superimposed upon these stable baseline differences. Future directions for research in this area are discussed.

Ongoing brain activity can be recorded as electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific EEG features and 2) exploring the efficacy of the classifiers. Support vector machine was employed to classify four emotional states (joy, anger, sadness, and pleasure) and obtained an averaged classification accuracy of 82.29% +/- 3.06% across 26 subjects. Further, this study identified 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics during music listening. The identified features were primarily derived from electrodes placed near the frontal and the parietal lobes, consistent with many of the findings in the literature. This study might lead to a practical system for noninvasive assessment of the emotional states in practical or clinical applications.