Towards Unified Neural Decoding with Brain Functional Network Modeling

• URL: http://arxiv.org/abs/2506.12055v1
• Date: Fri, 30 May 2025 12:10:37 GMT
• Title: Towards Unified Neural Decoding with Brain Functional Network Modeling
• Authors: Di Wu, Linghao Bu, Yifei Jia, Lu Cao, Siyuan Li, Siyu Chen, Yueqian Zhou, Sheng Fan, Wenjie Ren, Dengchang Wu, Kang Wang, Yue Zhang, Yuehui Ma, Jie Yang, Mohamad Sawan,
• Abstract summary: We present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework.<n>MIBRAIN constructs a whole functional brain network model by integrating intracranial neurophysiological recordings across multiple individuals.<n>Our framework paves the way for robust neural decoding across individuals and offers insights for practical clinical applications.
• Score: 34.13766828046489
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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