A foundation model with multi-variate parallel attention to generate neuronal activity

Learning from multi-variate time-series with heterogeneous channel configurations
remains a fundamental challenge for deep neural networks, particularly in clinical
domains such as intracranial electroencephalography (iEEG), where channel setups
vary widely across subjects. In this work, we introduce multi-variate parallel atten tion (MVPA), a novel self-attention mechanism that disentangles content, temporal,
and spatial attention, enabling flexible, generalizable, and efficient modeling of
time-series data with varying channel counts and configurations. We use MVPA to
build MVPFormer, a generative foundation model for human electrophysiology,
trained to predict the evolution of iEEG signals across diverse subjects. To support
this and future efforts by the community, we release the SWEC iEEG dataset, the
largest publicly available iEEG dataset to date, comprising nearly 10,000 hours of
recordings from heterogeneous clinical sources. MVPFormer leverages MVPA to
achieve strong generalization across subjects, demonstrating expert-level perfor mance in several iEEG tasks. MVPFormer surpasses state-of-the-art Transformer
baselines in seizure detection across the SWEC, the MAYO, and the FNUSA
datasets, while also achieving state-of-the-art performance on four Brain TreeBank
iEEG decoding tasks (volume, pitch, onset, and speech). We further validate MVPA
on standard time-series forecasting and classification tasks, where it matches or
exceeds the performance of existing attention-based models. Together, our contribu tions establish MVPA as a general-purpose attention mechanism for heterogeneous
time-series and MVPFormer as the first open-source, open-weights, and open-data
iEEG foundation model with SOTA clinical performance. The code is available at
https://github.com/IBM/multi-variate-parallel-transformer. The
SWEC iEEG dataset is available at https://huggingface.co/datasets/
NeuroTec/SWEC_iEEG_Dataset