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

arXiv: https://arxiv.org/abs/2506.19141 · HTML: https://mlsanity.com/paper/2506.19141/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [Machine-Learning-Based Diagnostics of EEG Pathology](https://mlsanity.com/paper/2002.05115/) (2020)
- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets](https://mlsanity.com/paper/2202.12950/) (2022)
- [Scaling laws for decoding images from brain activity](https://mlsanity.com/paper/2501.15322/) (2025)
- [Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery](https://mlsanity.com/paper/2503.00580/) (2025)
- [Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX](https://mlsanity.com/paper/2207.12369/) (2022)
- [Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods](https://mlsanity.com/paper/2212.08744/) (2022)
- [Decoding individual words from non-invasive brain recordings across 723 participants](https://mlsanity.com/paper/2412.17829/) (2024)
- [LEAD: An EEG Foundation Model for Alzheimer's Disease Detection](https://mlsanity.com/paper/2502.01678/) (2025)
- [Evaluating the structure of cognitive tasks with transfer learning](https://mlsanity.com/paper/2308.02408/) (2023)
- [How EEG preprocessing shapes decoding performance](https://mlsanity.com/paper/2410.14453/) (2024)
- [Is Limited Participant Diversity Impeding EEG-based Machine Learning?](https://mlsanity.com/paper/2503.13497/) (2025)
- [Are foundation models useful feature extractors for electroencephalography analysis?](https://mlsanity.com/paper/2502.21086/) (2025)

## Built on by (most cited first, 12 of 12)

- [EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models](https://mlsanity.com/paper/2508.17742/) (2025)
- [Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning](https://mlsanity.com/paper/2509.24222/) (2025)
- [Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes](https://mlsanity.com/paper/2604.23091/) (2026)
- [Mitigating Subject Dependency in EEG Decoding with Subject-Specific Low-Rank Adapters](https://mlsanity.com/paper/2510.08059/) (2025)
- [Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods](https://mlsanity.com/paper/2604.27033/) (2026)
- [The Identity Trap in EEG Foundation Models: A Diagnostic Audit](https://mlsanity.com/paper/2606.06647/) (2026)
- [Cross-Temporal Attention Fusion (CTAF) for Multimodal Physiological Signals in Self-Supervised Learning](https://mlsanity.com/paper/2602.02784/) (2026)
- [From Clever Hans to Scientific Discovery: Interpreting EEG Foundational Transformers with LRP](https://mlsanity.com/paper/2605.11885/) (2026)
- [MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data](https://mlsanity.com/paper/2602.18253/) (2026)
- [Behavioral Latency as Weak Event-Time Supervision for EEG Reaction-Time Decoding](https://mlsanity.com/paper/2608.29428/) (2026)
- [PhysioTRACE: Provenance-Aware Stress Tests for Physiological Foundation Models](https://mlsanity.com/paper/2609.34466/) (2026)
- [Representational and Functional Robustness to Electrode Montages in EEG Foundation Models](https://mlsanity.com/paper/2609.36288/) (2026)

## Abstract (arXiv)

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.