Interpretable Multi-View Dynamic Graph Learning for Intention Prediction Via Information Bottleneck

Predicting intention before behavioral onset provides valuable lead time for responsive brain-computer interfaces. However, pre-behavior neural activity exhibits nonstationary spectral dynamics, evolving functional connectivity, and redundant graph structures, posing challenges to both predictive performance and interpretability. We propose an interpretable dynamic graph learning framework for anticipatory intention prediction using ECoG recordings from non-human primates. The framework constructs a multi-view spatio-temporal-frequency representation, models the evolving coupling between neural features and functional topology through dynamic graphs, and learns dynamic predictive subgraphs with an information bottleneck motivated objective. On the vocalization prediction task, our framework achieves $95.55\%$ accuracy and compares favorably with 20 representative baselines. Further analyses reveal distinct predictive neural structures between rest and vocal preparation and show that these structures reorganize throughout the pre-vocalization interval, with window-specific spectral dynamics providing informative predictive evidence, supporting interpretable advance prediction for responsive brain-computer interfaces.