# OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models

arXiv: https://arxiv.org/abs/2606.00815 · HTML: https://mlsanity.com/paper/2606.00815/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 69 references that are in the map.
- First posted on arXiv 30 May 2026.

## Builds on (most cited first, 18 of 18)

- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data](https://mlsanity.com/paper/2101.12037/) (2021)
- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals](https://mlsanity.com/paper/2409.00101/) (2024)
- [REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects](https://mlsanity.com/paper/2510.21585/) (2025)
- [Neuro-GPT: Towards A Foundation Model for EEG](https://mlsanity.com/paper/2311.03764/) (2023)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis](https://mlsanity.com/paper/2002.09283/) (2020)
- [EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising](https://mlsanity.com/paper/2009.11662/) (2020)
- [A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG](https://mlsanity.com/paper/1611.08492/) (2016)
- [Improved EEG Event Classification Using Differential Energy](https://mlsanity.com/paper/1801.02477/) (2018)
- [EEG Foundation Models: A Critical Review of Current Progress and Future Directions](https://mlsanity.com/paper/2507.11783/) (2025)
- [BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals](https://mlsanity.com/paper/2505.18185/) (2025)
- [Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery](https://mlsanity.com/paper/2503.00580/) (2025)
- [AdaBrain-Bench: Benchmarking Brain Foundation Models for Brain-Computer Interface Applications](https://mlsanity.com/paper/2507.09882/) (2025)
- [FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model](https://mlsanity.com/paper/2502.06438/) (2025)
- [BETA: A Large Benchmark Database Toward SSVEP-BCI Application](https://mlsanity.com/paper/1911.13045/) (2019)
- [EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation](https://mlsanity.com/paper/2404.01008/) (2024)

## Built on by (most cited first, 8 of 8)

- [A Negative-Control Protocol for Clinical EEG Foundation-Model Benchmarks: Dataset Identity and External-Cohort Stress Testing](https://mlsanity.com/paper/2607.24519/) (2026)
- [Understanding and Correcting Low-Frequency Bias in EEG Foundation Model](https://mlsanity.com/paper/2608.01898/) (2026)
- [Brain-to-Language Decoding: Tasks, Signals, Methods, Evaluation, Practical Use and Beyond](https://mlsanity.com/paper/2609.27650/) (2026)
- [BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understanding](https://mlsanity.com/paper/2608.04156/) (2026)
- [Beyond Flattened Tokens: Structure-Preserving EEG Decoding with Reusable TriDim Blocks](https://mlsanity.com/paper/2609.19842/) (2026)
- [Matched-Input Estimates Differ in Sign Across Architectures: Auditing EEG Foundation Models on Motor Imagery](https://mlsanity.com/paper/2609.23924/) (2026)
- [Benchmarking EEG Foundation Models at Scale: Lessons from 20,000 Evaluations](https://mlsanity.com/paper/2609.32743/) (2026)
- [GeoNIRS: Geometry-Aware Pretraining for Heterogeneous fNIRS Representation Learning](https://mlsanity.com/paper/2610.17845/) (2026)

## Abstract (arXiv)

Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions. EEG foundation models are emerging, but evaluation remains fragmented due to heterogeneous datasets and nconsistent task protocols. Here, we introduce OmniEEG-Bench, a unified benchmark and downstream task roadmap for EEG foundation models (FMs). It organizes evaluation of EEG FMs into six task families spanning (i) signal reliability, (ii) biometrics and disease, (iii) consciousness and state, (iv) cognition and emotion, (v) naturalistic stimulus decoding, and (vi) motor and interaction, introducing a new generation of tasks not systematically benchmarked in prior EEG FM work. OmniEEG-Bench standardizes model deployment, task definitions, and metrics through a task-card specification, and unifies 54 EEG datasets with consistent evaluation protocols. We benchmark 10 representative EEG foundation models and report a leaderboard that covers diverse evaluation settings. Both pretraining dataset diversity and model size are significantly associated with better average ranks across datasets, revealing scaling-law behavior in EEG foundation models (Figure 1). These results suggest that scaling EEG foundation models requires not only larger architectures but also broader and more diverse pretraining data. The benchmark code is available at https://github.com/ncclab-sustech/omni-eegbench.git.