Towards Unified Neural Decoding with Brain Functional Network Modeling

TL;DR

This paper introduces MIBRAIN, a novel neural decoding framework that integrates intracranial recordings from multiple individuals to improve inter-individual brain decoding accuracy and generalization, validated through Mandarin syllable articulation tasks.

Contribution

MIBRAIN is the first framework to construct a whole brain network model from multi-individual intracranial data, enabling robust, generalized neural decoding across subjects.

Findings

Significant improvement in articulation decoding accuracy.

Enhanced generalization to unseen subjects.

Effective neural predictions for regions without electrodes.

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.