Continuous Emotion Recognition during Music Listening Using EEG Signals: A Fuzzy Parallel Cascades Model

TL;DR

This paper introduces a fuzzy parallel cascades model for continuous emotion recognition from EEG signals during music listening, outperforming traditional models in predicting valence and arousal with lower error rates.

Contribution

The study proposes a novel fuzzy parallel cascades model that effectively captures dynamic emotional states from EEG data, improving prediction accuracy over existing methods.

Findings

FPC model achieved lower RMSE than LR, SVR, and LSTM RNN.

Frontal EEG theta band correlates with valence.

Dynamic modeling of emotional appraisal is feasible for music emotion recognition.

Abstract

A controversial issue in artificial intelligence is human emotion recognition. This paper presents a fuzzy parallel cascades (FPC) model for predicting the continuous subjective appraisal of the emotional content of music by time-varying spectral content of EEG signals. The EEG, along with an emotional appraisal of 15 subjects, was recorded during listening to seven musical excerpts. The emotional appraisement was recorded along the valence and arousal emotional axes as a continuous signal. The FPC model was composed of parallel cascades with each cascade containing a fuzzy logic-based system. The FPC model performance was evaluated by comparing with linear regression (LR), support vector regression (SVR) and Long Short Term Memory recurrent neural network (LSTM RNN) models. The RMSE of the FPC was lower than other models for the estimation of both valence and arousal of all musical excerpts. The lowest RMSE was 0.089 which was obtained in estimation of the valence of MS4 by the FPC model. The analysis of MI of frontal EEG with the valence confirms the role of frontal channels in theta frequency band in emotion recognition. Considering the dynamic variations of musical features during songs, employing a modeling approach to predict dynamic variations of the emotional appraisal can be a plausible substitute for the classification of musical excerpts into predefined labels.