Unified model learns brain signals from surface to spikes
iBrain: A Unified Foundation Model Reading the Brain from Surface to Spikes
Artificial Intelligence
Summary
Measuring brain activity can be done in different ways, like recording electrical signals from the brain’s surface or individual neurons. The team behind this work created a single computer model called iBrain that learns from both surface signals and neuron spikes together. By training on a large amount of brain data, iBrain can better understand brain activity than models trained on only one type of signal. This approach may help build tools that work well across different brain recording methods and tasks.
intracranial EEG (iEEG)spiking activityneural recordingsfoundation modelTransformerpretrainingmasked signal reconstructionchannel-view alignmentneural dynamicstransfer learning
Authors
Ying Chen, Tiou Wang, Zhifeng Yue
Abstract
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.