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

TL;DR

This paper introduces a large-scale EEG decoding challenge with two tasks: zero-shot cross-task and cross-subject decoding, and mental health prediction, using a massive dataset to advance generalizable EEG models.

Contribution

It presents a novel large-scale EEG dataset and a competitive framework for developing models that generalize across tasks and subjects, and predict mental health traits.

Findings

Baseline neural network models provided for each challenge.

The dataset enables testing of zero-shot decoding capabilities.

Potential for identifying biomarkers for mental health.

Abstract

Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenges. First, the Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data. Second, the Psychopathology factor prediction Challenge asks participants to infer subject 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 child to young adult 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 generalise across tasks and individuals will pave the way for ML network architectures capable of adapting to EEG data collected from diverse tasks and individuals. Similarly, predicting mental health-relevant personality trait values from EEG might identify objective biomarkers useful for clinical diagnosis and design of personalised treatment for psychological conditions. Ultimately, the advances spurred by this challenge could contribute to the development of computational psychiatry and useful neurotechnology, and contribute to breakthroughs in both fundamental neuroscience and applied clinical research.