Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition

TL;DR

This paper introduces MSBAM, a CNN-based model that leverages multi-scale and asymmetric EEG features, inspired by neural hemisphere differences, to achieve high accuracy in emotion recognition across multiple emotional dimensions.

Contribution

The paper presents a novel CNN model, MSBAM, that simultaneously explores multi-scale and asymmetric EEG features based on neural hemisphere activity for improved emotion recognition.

Findings

Achieved over 99% accuracy on DEAP and DREAMER datasets.

Effectively classified emotional states across four dimensions.

Demonstrated the importance of multi-scale and asymmetric features in EEG-based emotion recognition.

Abstract

In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) field. The neuroscience researches indicate that the left and right brain hemispheres demonstrate activity differences under different emotional activities, which could be an important principle for designing deep learning (DL) model for emotion recognition. Besides, owing to the nonstationarity of EEG signals, using convolution kernels of a single size may not sufficiently extract the abundant features for EEG classification tasks. Based on these two angles, we proposed a model termed Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM) based on convolutional neural network (CNN) structure. Evaluated on the public DEAP and DREAMER datasets, MSBAM achieved over 99% accuracy for the two-class classification of low-level and high-level states in each of four emotional dimensions, i.e., arousal, valence, dominance and liking, respectively. This study further demonstrated the promising potential to design the DL model from the multi-scale characteristics of the EEG data and the neural mechanisms of the emotion cognition.