The 2025 EEG Decoding Challenge: From Cross-Task to Learning Subject Invariance Representation for 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 Invariance Representation: Creating robust representations that generalize across different subjects while predicting clinical factors

Check the complete proposal here

Competition Tasks

Task 1: Cross-Task Transfer Learning

Participants will train models on passive EEG tasks (Resting State, Surround Suppression, Movie Watching) and evaluate their performance on active tasks (Contrast Change Detection, Sequence Learning, Symbol Search). The goal is to develop models that can effectively transfer knowledge across different cognitive tasks.

Task 2: Subject Invariance Representation

Teams will develop models that can predict clinical factors (p-factor, internalizing, externalizing, and attention) while maintaining robustness across different subjects. This task focuses on creating subject-invariant representations that generalize well to unseen individuals.

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

Institution Organization

Core Coordinators