Empowering Brain-Computer Interface Decoding with Large Language Models

Decoding brain signals supports a broad range of brain–computer interface (BCI) applications spanning cognitive, motor, and clinical domains. These tasks vary substantially in their targeted brain states, neural patterns, and output spaces. Despite recent advances in brain foundation models, the field still relies on disjointed prediction heads and task-specific adaptation pipelines, limiting unified knowledge sharing and robust generalization across tasks. To overcome these limitations, we introduce UniMind, a unified multi-task brain decoding framework driven by large language models (LLMs). UniMind reformulates heterogeneous task outputs into a unified question–answer paradigm, casting diverse neural signals into a universal semantic manifold optimized via a single shared autoregressive objective. To reconcile task heterogeneity within a single model, we design a task-aware query selector to dynamically extract task-relevant representations. Furthermore, a neuro-language connector is proposed to distill high-dimensional spatiotemporal brain patterns into compact neural-semantic representations directly interpretable by LLMs. Across ten scalp EEG datasets spanning five task domains, UniMind achieves an average improvement of 8.94 percentage points in balanced accuracy. It shows strong cross-subject performance and generalizes to unseen datasets. Beyond scalp EEG, we show that UniMind also benefits intracortical spike decoding in non-human primates, raising average $R^2$ and producing more separable spike representations. These results establish UniMind as a scalable framework for unified neural decoding across diverse subjects, tasks, and recording modalities.