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. The source code and pre-trained weights are available at: https://github.com/yzz673/Brant-2.

翻译：基础模型通过在海量无标注数据上进行预训练获得优势，并能在少量标注数据下实现多种应用场景的优异性能。这类模型在分析脑信号方面尤为有效，因为该领域涉及众多应用场景，且大规模标注成本高昂。本文提出了目前规模最大的脑信号基础模型Brant-2。相较于针对颅内神经信号设计的基础模型Brant，Brant-2不仅对数据变化和建模尺度具有更强的鲁棒性，还能应用于更广泛的脑神经数据。通过大量实验任务验证，我们证明Brant-2能够适应脑信号领域的多种应用场景。进一步分析揭示了Brant-2的可扩展性，验证了各组件的有效性，并展示了模型在标签稀缺情况下维持性能的能力。源代码与预训练权重已开源：https://github.com/yzz673/Brant-2。