UniBCI: Towards a Unified Pretrained Model for Invasive Brain-Computer Interfaces

Modeling invasive neural spike data is fundamental to advancing high-performance brain-computer interfaces (BCIs). However, existing approaches face critical challenges, including limited-scale heterogeneous data, cross-conditions distribution shift, and the intrinsic spatiotemporal complexity of invasive neural signals. In this work, we construct a pretraining corpus spanning multiple species, subjects, brain regions, and behavioral experiment paradigms. These heterogeneous recordings are standardized via our proposed unified normalization and tokenization. We also propose UniBCI, a unified pretrained model for invasive Brain-Computer Interfaces. The model integrates three key components: (1) a context-conditioned spatio-temporal tokenization (CST) scheme that embeds neural signals together with metadata into a shared representation space; (2) a hierarchical Interval-Area Attention (IAA) mechanism that captures patterns of spike dynamics in slots via linear attention and locality dependencies via sliding-window attention; and (3) a self-supervised masked signals reconstruction objective for rebuilding spike trains from unmasked neural tokens. We demonstrate that UniBCI outperforms baselines trained without behavioral data across both classification and regression tasks, while achieving performance comparable to leading behavior-supervised methods with fewer trainable parameters and lower inference latency. These results suggest that UniBCI provides a potential step toward general-purpose neural foundation models, enabling robust, scalable, and generalizable representation learning for invasive neural data. The code for this paper is available at: https://anonymous.4open.science/r/UniBCI-C805.