Foundation Models for the Brain and Body

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 neural technology, including EEG, intracortical electrophysiology, fMRI, EMG, MEG, and ECG, have enabled the broad collection of biosignals 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 consume incredibly large and diverse datasets with the goal of generalizing across diverse downstream applications, from brain-computer interfacing to health monitoring and robotics. Realizing this potential, however, requires addressing the unique challenges that come with the recordings available from these modalities: they are noisy and heterogeneous timeseries that were collected under variable conditions across subjects, devices, and environments. To get truly generalist foundation models we need to meet these challenges. To this end, 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.