{"ID":2853964,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17879","arxiv_id":"2510.17879","title":"Decoding Listeners Identity: Person Identification from EEG Signals Using a Lightweight Spiking Transformer","abstract":"EEG-based person identification enables applications in security, personalized brain-computer interfaces (BCIs), and cognitive monitoring. However, existing techniques often rely on deep learning architectures at high computational cost, limiting their scope of applications. In this study, we propose a novel EEG person identification approach using spiking neural networks (SNNs) with a lightweight spiking transformer for efficiency and effectiveness. The proposed SNN model is capable of handling the temporal complexities inherent in EEG signals. On the EEG-Music Emotion Recognition Challenge dataset, the proposed model achieves 100% classification accuracy with less than 10% energy consumption of traditional deep neural networks. This study offers a promising direction for energy-efficient and high-performance BCIs. The source code is available at https://github.com/PatrickZLin/Decode-ListenerIdentity.","short_abstract":"EEG-based person identification enables applications in security, personalized brain-computer interfaces (BCIs), and cognitive monitoring. However, existing techniques often rely on deep learning architectures at high computational cost, limiting their scope of applications. In this study, we propose a novel EEG person...","url_abs":"https://arxiv.org/abs/2510.17879","url_pdf":"https://arxiv.org/pdf/2510.17879v1","authors":"[\"Zheyuan Lin\",\"Siqi Cai\",\"Haizhou Li\"]","published":"2025-10-17T08:20:01Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":608102,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2853964,"paper_url":"https://arxiv.org/abs/2510.17879","paper_title":"Decoding Listeners Identity: Person Identification from EEG Signals Using a Lightweight Spiking Transformer","repo_url":"https://github.com/PatrickZLin/Decode-ListenerIdentity","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}