Foundation Models for the Brain and Body 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.