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

• URL: http://arxiv.org/abs/2506.16448v1
• Date: Thu, 19 Jun 2025 16:33:31 GMT
• Title: Consumer-friendly EEG-based Emotion Recognition System: A Multi-scale Convolutional Neural Network Approach
• Authors: Tri Duc Ly, Gia H. Ngo,
• Abstract summary: EEG is a non-invasive, safe, and low-risk method to record electrophysiological signals inside the brain.<n>We propose a novel approach to utilize multi-scale convolutional neural networks to accomplish such tasks.
• Score: 1.3812010983144802
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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