Towards Bi-Hemispheric Emotion Mapping through EEG: A Dual-Stream Neural Network Approach

TL;DR

This paper introduces a dual-stream neural network leveraging bi-hemispheric EEG data to improve emotion recognition accuracy, validated through VR-based emotion mimicry experiments and temporal analysis of EEG signals.

Contribution

It presents a novel bi-hemispheric dual-stream neural network architecture for EEG-based emotion mapping, surpassing baseline methods and emphasizing the importance of temporal signal intervals.

Findings

Enhanced emotion recognition accuracy over baseline methods

Specific EEG signal intervals at stimulus start and end are crucial

Temporal analysis improves understanding of emotion-related brain activity

Abstract

Emotion classification through EEG signals plays a significant role in psychology, neuroscience, and human-computer interaction. This paper addresses the challenge of mapping human emotions using EEG data in the Mapping Human Emotions through EEG Signals FG24 competition. Subjects mimic the facial expressions of an avatar, displaying fear, joy, anger, sadness, disgust, and surprise in a VR setting. EEG data is captured using a multi-channel sensor system to discern brain activity patterns. We propose a novel two-stream neural network employing a Bi-Hemispheric approach for emotion inference, surpassing baseline methods and enhancing emotion recognition accuracy. Additionally, we conduct a temporal analysis revealing that specific signal intervals at the beginning and end of the emotion stimulus sequence contribute significantly to improve accuracy. Leveraging insights gained from this temporal analysis, our approach offers enhanced performance in capturing subtle variations in the states of emotions.