Title: Unlocking Volition: Proactive Intention Decoding via Interpretable Graph Learning of Multi-Region ECoG
Primary area: applications to neuroscience & cognitive science
Keywords: NEUROSCIENCE; Cognitive science; ECoG;Non-human primates;Intention prediction

Abstract: Current brain–machine interfaces (BMI), face fundamental limitations due to inherent latency from reliance on delayed motor cortical signals, and computational overhead, restricting their effectiveness in real-time applications such as rehabilitation therapy. Recent neuroscience indicates prefrontal and sensory cortical activities precede motor execution, offering an opportunity for proactive intent prediction. However, challenges remain in acquiring multi-region neural data, efficiently decoding high-dimensional signals, and ensuring model interpretability. To address these, we developed a high-density electrocorticography (ECoG)-based paradigm based on marmosets and introduced an information-bottleneck-driven graph transformer (ECoG-IBGT), reframing neural decoding as graph classification. Our method achieves 99.29\% accuracy up to 400 ms before action onset with inherent interpretability, laying a foundation for reliable, low-latency BMIs. Code is available at  [*********************************URL Blinded for Review*********************************].

TL;DR: ECoG-IBGT leverages interpretable multi-region ECoG graph representations to anticipate intention (400 ms prior to onset), substantially reducing system-level latency in BMIs.