Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information

TL;DR

This paper introduces a CNN-based method for EEG emotion recognition that leverages novel brain connectivity features and asymmetric activity patterns, significantly improving recognition accuracy.

Contribution

It presents a new deep learning framework utilizing brain connectivity and spatial information for enhanced EEG-based emotion recognition.

Findings

Improved emotion recognition accuracy using connectivity features

Effective capture of asymmetric brain activity patterns

Validation on experimental data confirms approach's effectiveness

Abstract

Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric services. However, there is still much room for improvement, particularly in terms of the recognition accuracy. In this paper, we propose a novel deep learning approach using convolutional neural networks (CNNs) for EEG-based emotion recognition. In particular, we employ brain connectivity features that have not been used with deep learning models in previous studies, which can account for synchronous activations of different brain regions. In addition, we develop a method to effectively capture asymmetric brain activity patterns that are important for emotion recognition. Experimental results confirm the effectiveness of our approach.