EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding

Current electroencephalogram (EEG) decoding models are typically trained on specific subjects and specific tasks. Here, we introduce a large-scale, code-submission-based competition to subsume this approach through two challenges. First, the transfer challenge consists of building a model that can zero-shot decode new tasks and new subjects from their EEG. Second, the psychopathology factor prediction challenge consists of predicting measures of mental health from EEG data. For this, we use an unprecedented, multi-terabyte dataset of high-density EEG signals (128 channels) recorded from over 3,000 subjects engaged in multiple active and passive tasks. We provide several tunable neural network baselines for each of these two challenges, including a simple network and demographic-based regression models. Developing models that generalize across tasks and individuals will pave the way for EEG architectures capable of adapting to diverse tasks and individuals. Similarly, predicting mental health dimensions from EEG will be essential to systematically identify objective biomarkers for clinical diagnosis and personalized treatment. Ultimately, the advances spurred by this challenge are poised to shape the future of neurotechnology and computational psychiatry, catalyzing breakthroughs in both fundamental neuroscience and applied clinical research.