EEG-Based Emotion Recognition in Music Listening

TLDR

EEG records ongoing brain activity, enabling investigation of links between emotional states and neural activity. The study aimed to develop a machine‑learning framework that classifies EEG dynamics into self‑reported emotional states during music listening by identifying emotion‑specific features and evaluating classifier performance. The authors used machine‑learning classifiers on EEG recordings from 26 subjects, identified 30 subject‑independent emotion‑specific features, and investigated whether a reduced electrode set could capture the dynamics. Support vector machines achieved an average accuracy of 82.29 % ± 3.06 % in classifying four emotions, with 30 subject‑independent features—mainly from frontal and parietal electrodes—identified, suggesting a feasible, reduced‑electrode, noninvasive emotion‑recognition system.

Abstract

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.