Towards Unified Neural Decoding with Brain Functional Network Modeling
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915342999093248 |
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| author | Wu, Di Bu, Linghao Jia, Yifei Cao, Lu Li, Siyuan Chen, Siyu Zhou, Yueqian Fan, Sheng Ren, Wenjie Wu, Dengchang Wang, Kang Zhang, Yue Ma, Yuehui Yang, Jie Sawan, Mohamad |
| author_facet | Wu, Di Bu, Linghao Jia, Yifei Cao, Lu Li, Siyuan Chen, Siyu Zhou, Yueqian Fan, Sheng Ren, Wenjie Wu, Dengchang Wang, Kang Zhang, Yue Ma, Yuehui Yang, Jie Sawan, Mohamad |
| contents | Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that constructs a whole functional brain network model by integrating intracranial neurophysiological recordings across multiple individuals. MIBRAIN leverages self-supervised learning to derive generalized neural prototypes and supports group-level analysis of brain-region interactions and inter-subject neural synchrony. To validate our framework, we recorded stereoelectroencephalography (sEEG) signals from a cohort of individuals performing Mandarin syllable articulation. Both real-time online and offline decoding experiments demonstrated significant improvements in both audible and silent articulation decoding, enhanced decoding accuracy with increased multi-subject data integration, and effective generalization to unseen subjects. Furthermore, neural predictions for regions without direct electrode coverage were validated against authentic neural data. Overall, this framework paves the way for robust neural decoding across individuals and offers insights for practical clinical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12055 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Unified Neural Decoding with Brain Functional Network Modeling Wu, Di Bu, Linghao Jia, Yifei Cao, Lu Li, Siyuan Chen, Siyu Zhou, Yueqian Fan, Sheng Ren, Wenjie Wu, Dengchang Wang, Kang Zhang, Yue Ma, Yuehui Yang, Jie Sawan, Mohamad Neurons and Cognition Artificial Intelligence Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that constructs a whole functional brain network model by integrating intracranial neurophysiological recordings across multiple individuals. MIBRAIN leverages self-supervised learning to derive generalized neural prototypes and supports group-level analysis of brain-region interactions and inter-subject neural synchrony. To validate our framework, we recorded stereoelectroencephalography (sEEG) signals from a cohort of individuals performing Mandarin syllable articulation. Both real-time online and offline decoding experiments demonstrated significant improvements in both audible and silent articulation decoding, enhanced decoding accuracy with increased multi-subject data integration, and effective generalization to unseen subjects. Furthermore, neural predictions for regions without direct electrode coverage were validated against authentic neural data. Overall, this framework paves the way for robust neural decoding across individuals and offers insights for practical clinical applications. |
| title | Towards Unified Neural Decoding with Brain Functional Network Modeling |
| topic | Neurons and Cognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.12055 |