  Foundational models benefit from pre-training on large amounts of unlabeled
data and enable strong performance in a wide variety of applications with a
small amount of labeled data. Such models can be particularly effective in
analyzing brain signals, as this field encompasses numerous application
scenarios, and it is costly to perform large-scale annotation. In this work, we
present the largest foundation model in brain signals, Brant-2. Compared to
Brant, a foundation model designed for intracranial neural signals, Brant-2 not
only exhibits robustness towards data variations and modeling scales but also
can be applied to a broader range of brain neural data. By experimenting on an
extensive range of tasks, we demonstrate that Brant-2 is adaptive to various
application scenarios in brain signals. Further analyses reveal the scalability
of the Brant-2, validate each component's effectiveness, and showcase our
model's ability to maintain performance in scenarios with scarce labels.
