Consumer-friendly EEG-based Emotion Recognition System: A Multi-scale Convolutional Neural Network Approach
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912441892339712 |
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| author | Ly, Tri Duc Ngo, Gia H. |
| author_facet | Ly, Tri Duc Ngo, Gia H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16448 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Consumer-friendly EEG-based Emotion Recognition System: A Multi-scale Convolutional Neural Network Approach Ly, Tri Duc Ngo, Gia H. Machine Learning Artificial Intelligence 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. |
| title | Consumer-friendly EEG-based Emotion Recognition System: A Multi-scale Convolutional Neural Network Approach |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.16448 |