The 2025 EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding is a biosignal challenge accepted to the NeurIPS 2025 Competition Track. This competition aims to advance the field of EEG decoding by addressing two critical challenges:

1. Cross-Task Transfer Learning: Developing models that can effectively transfer knowledge from passive EEG tasks to active tasks
2. Subject Invariant Representation: Creating robust representations that generalize across different subjects while predicting clinical factors

Check the complete proposal here

Competition Tasks

Challenge 1: Cross-Task Transfer Learning

This supervised learning challenge combines regression and classification objectives. Participants will predict behavioral performance metrics (response time via regression and success rate via classification) from an active experimental paradigm (Contrast Change Detection, CCD) using EEG data from a passive paradigm (Surround Suppression, SuS). Teams can leverage multiple datasets and experimental paradigms to train their models, utilizing unsupervised or self-supervised pretraining to capture latent EEG representations, then fine-tuning for the specific supervised objectives to achieve generalization across subjects and cognitive paradigms. See the Starter Kit for more details.

Challenge 2: Psychopathology Factor Prediction (Subject Invariant Representation)

This supervised regression challenge requires teams to predict four continuous psychopathology scores (p-factor, internalizing, externalizing, and attention) from EEG recordings across multiple experimental paradigms. Teams can employ unsupervised or self-supervised pretraining strategies to learn generalizable neural representations, then adapt these foundation models for the regression targets while maintaining robustness across different subjects and experimental conditions. See the Starter Kit for more details.

Dataset

The competition uses the HBN-EEG dataset (paper, blog post), which includes EEG recordings from over 3,000 participants across six distinct cognitive tasks:

Passive Tasks

• Resting State (RS): Eyes open/closed conditions with fixation cross
• Surround Suppression (SuS): Four flashing peripheral disks with contrasting background
• Movie Watching (MW): Four short films with different themes

Active Tasks

• Contrast Change Detection (CCD): Identifying dominant contrast in co-centric flickering grated disks
• Sequence Learning (SL): Memorizing and reproducing sequences of flashed circles
• Symbol Search (SyS): Computerized version of WISC-IV subtest

Each participant’s data is accompanied by four psychopathology dimensions derived from the Child Behavior Checklist (CBCL) and demographic information, including age, sex, and handedness. Data is in the BIDS (Brain Imaging Data Structure) format.

Workshop

Top-ranking teams will be invited to present their work at a dedicated workshop during the NeurIPS 2025 conference (December 14-15, 2025).

Motivation

EEG decoding faces significant challenges due to signal heterogeneity from various factors like non-stationarity, noise sensitivity, inter-subject morphological differences, varying experimental paradigms, and differences in sensor placement. While recent advances in machine learning have shown promise, there remains a critical need for models that can generalize across different subjects and tasks without expensive recalibration.

This competition aims to address these challenges by:

1. Providing a large-scale, diverse dataset for evaluating model generalization
2. Encouraging the development of robust, transferable EEG decoding methods
3. Establishing benchmarks for cross-task and cross-subject performance
4. Fostering collaboration between machine learning and neuroscience communities

Organizing Institutions

Core Coordinators