Foundation Models for the Brain and Body

NeurIPS 2026 Workshop

Our brains and bodies speak a rich and complex biological language of neural and physiological signals, a language that AI models are increasingly capable of deciphering as large-scale datasets become available. Recent advances in brain interfacing and wearable technologies, including EEG, intracortical electrophysiology, EMG, MEG, and ECG, have enabled the broad collection of these signals across real-world contexts and diverse populations. This growing wealth of data is driving a shift toward foundation models: large-scale, pretrained AI systems designed to learn from biosignals and generalize across diverse downstream applications, from brain-computer interfacing to health monitoring.

Realizing this potential, however, requires addressing the unique challenges that come with biosignal timeseries: they are noisy, heterogeneous, and collected under variable conditions across subjects, devices, and environments. To meet these challenges, this workshop brings together neuroscientists, biomedical engineers, wearable tech researchers, and machine learning experts advancing foundation model approaches. Through interdisciplinary dialogue, we aim to catalyze the next generation of AI models that can capture the complexity of the brain, body, and behavior at scale. This year we broaden the scope toward embodied intelligence: foundation models for movement, motor control, and closed-loop interaction between brain, body, and machine. We also turn attention to how progress is measured. Evaluation has matured unevenly across subfields: EEG has developed mature benchmarks and large-scale challenges, while other modalities still lack agreed-upon protocols for testing whether pretraining genuinely helps. This year we are adding a dedicated panel discussion on benchmarking, bringing together authors of influential benchmarking efforts to share which evaluation practices have proven effective and which pitfalls to avoid, so that lessons hard-won in one modality can accelerate progress across the others.