Unlocking Volition: Proactive Intention Decoding via Interpretable Graph Learning of Multi-Region ECoG

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 that 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 the foundation for reliable, low-latency BMIs. The source code is available at https://anonymous.4open.science/r/ecog-ibgt-code-F2D2.