Consumer-friendly EEG-based Emotion Recognition System: A Multi-scale Convolutional Neural Network Approach

TL;DR

This paper introduces a multi-scale convolutional neural network for EEG-based emotion recognition, achieving improved accuracy in real-life scenarios using consumer-grade devices and novel feature extraction techniques.

Contribution

A novel multi-scale CNN architecture with specialized kernels for EEG emotion recognition, outperforming existing models in real-world conditions.

Findings

Outperforms TSception in predicting valence, arousal, and dominance.

Effective use of feature extraction kernels with ratio coefficients.

Model demonstrates robustness in real-life EEG emotion recognition.

Abstract

EEG is a non-invasive, safe, and low-risk method to record electrophysiological signals inside the brain. Especially with recent technology developments like dry electrodes, consumer-grade EEG devices, and rapid advances in machine learning, EEG is commonly used as a resource for automatic emotion recognition. With the aim to develop a deep learning model that can perform EEG-based emotion recognition in a real-life context, we propose a novel approach to utilize multi-scale convolutional neural networks to accomplish such tasks. By implementing feature extraction kernels with many ratio coefficients as well as a new type of kernel that learns key information from four separate areas of the brain, our model consistently outperforms the state-of-the-art TSception model in predicting valence, arousal, and dominance scores across many performance evaluation metrics.