Ensemble emotion recognizing with multiple modal physiological signals

TL;DR

This paper presents an ensemble classification model that fuses multiple physiological signals like EEG, EMG, and EOG to improve emotion recognition accuracy, demonstrating high performance on the DEAP dataset.

Contribution

It introduces a novel multi-modal physiological signal fusion approach with ensemble classification for emotion recognition, addressing limitations of single-signal methods.

Findings

Achieved 94.42% accuracy on arousal classification

Achieved 94.02% accuracy on valence classification

Average accuracy of 90.74% on four-class emotion recognition

Abstract

Physiological signals that provide the objective repression of human affective states are attracted increasing attention in the emotion recognition field. However, the single signal is difficult to obtain completely and accurately description for emotion. Multiple physiological signals fusing models, building the uniform classification model by means of consistent and complementary information from different emotions to improve recognition performance. Original fusing models usually choose the particular classification method to recognition, which is ignoring different distribution of multiple signals. Aiming above problems, in this work, we propose an emotion classification model through multiple modal physiological signals for different emotions. Features are extracted from EEG, EMG, EOG signals for characterizing emotional state on valence and arousal levels. For characterization, four bands filtering theta, beta, alpha, gamma for signal preprocessing are adopted and three Hjorth parameters are computing as features. To improve classification performance, an ensemble classifier is built. Experiments are conducted on the benchmark DEAP datasets. For the two-class task, the best result on arousal is 94.42\%, the best result on valence is 94.02\%, respectively. For the four-class task, the highest average classification accuracy is 90.74, and it shows good stability. The influence of different peripheral physiological signals for results is also analyzed in this paper.