发表机构
Chinese Institute for Brain Research（中国脑科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
iBrain是统一基础模型，联合预训练iEEG与尖峰信号，采用信号特定编码器和共享时空Transformer，在7000小时数据上预训练，性能优于单信号基线，具备可迁移性和数据效率。
AI 中文摘要
侵入式神经记录能够提供高保真度的大脑活动测量，其中颅内脑电图（iEEG）和皮层内尖峰活动等信号可在不同空间和时间尺度上捕捉神经动态。然而，现有的神经基础模型大多针对不同的侵入式记录范式独立开发，跨异质侵入式信号的联合预训练仍未得到充分探索。在本工作中，我们提出了iBrain，一个统一的基础模型，可联合学习iEEG和尖峰活动。iBrain采用信号特定的编码器以适应其不同的信号特征，并采用共享的时空Transformer骨干网络来建模记录通道和时间之间的依赖关系。我们使用掩码信号重建和通道视图对齐，在超过7,000小时的异质神经记录上对iBrain进行预训练，以促进神经动态的上下文建模以及跨不同通道的鲁棒性。iBrain在多个基准上持续优于单信号预训练基线，并达到最先进的性能。进一步的实验表明，iBrain在不同记录设置下展现出可迁移性和数据效率。这些结果凸显了在异质侵入式神经记录上进行联合预训练的潜力，以支持可扩展的神经建模和跨记录设置及下游任务的可迁移表示。
英文摘要
Invasive neural recordings provide high-fidelity measurements of brain activity, with signals such as intracranial EEG (iEEG) and intracortical spiking activity capturing neural dynamics at different spatial and temporal scales. Yet existing neural foundation models have largely been developed independently for different invasive recording paradigms, leaving joint pretraining across heterogeneous invasive signals underexplored. In this work, we introduce iBrain, a unified foundation model that jointly learns from iEEG and spiking activity. iBrain employs signal-specific encoders to accommodate their distinct signal characteristics and a shared spatiotemporal Transformer backbone to model dependencies across recording channels and time. We pretrain iBrain on over 7,000 hours of heterogeneous neural recordings using masked signal reconstruction and channel-view alignment, promoting contextual modeling of neural dynamics and robustness across different channels. iBrain consistently outperforms single-signal pretraining baselines and achieves state-of-the-art performance on multiple benchmarks. Further experiments demonstrate that iBrain exhibits transferability and data efficiency across diverse recording settings. These results highlight the potential of joint pretraining on heterogeneous invasive neural recordings to support scalable neural modeling and transferable representations across recording settings and downstream tasks.
