# AdaBrain-Bench: Benchmarking Brain Foundation Models for Brain-Computer Interface Applications

arXiv: https://arxiv.org/abs/2507.09882 · HTML: https://mlsanity.com/paper/2507.09882/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 72 references that are in the map.
- First posted on arXiv 14 Jul 2025.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://mlsanity.com/paper/2406.16860/) (2024)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Transformer-based Spatial-Temporal Feature Learning for EEG Decoding](https://mlsanity.com/paper/2106.11170/) (2021)
- [SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification](https://mlsanity.com/paper/2105.11043/) (2021)
- [Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion](https://mlsanity.com/paper/2403.07721/) (2024)
- [Inter-database validation of a deep learning approach for automatic sleep scoring](https://mlsanity.com/paper/2009.10365/) (2020)
- [A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG](https://mlsanity.com/paper/1611.08492/) (2016)
- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms](https://mlsanity.com/paper/2402.13005/) (2024)
- [LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition](https://mlsanity.com/paper/2410.09767/) (2024)

## Built on by (most cited first, 17 of 17)

- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)
- [EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models](https://mlsanity.com/paper/2508.17742/) (2025)
- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning](https://mlsanity.com/paper/2508.10298/) (2025)
- [DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI](https://mlsanity.com/paper/2601.06134/) (2026)
- [OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models](https://mlsanity.com/paper/2606.00815/) (2026)
- [NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces](https://mlsanity.com/paper/2605.14698/) (2026)
- [Foundation Models for Cross-Domain EEG Analysis Application: A Survey](https://mlsanity.com/paper/2508.15716/) (2025)
- [A Negative-Control Protocol for Clinical EEG Foundation-Model Benchmarks: Dataset Identity and External-Cohort Stress Testing](https://mlsanity.com/paper/2607.24519/) (2026)
- [The Identity Trap in EEG Foundation Models: A Diagnostic Audit](https://mlsanity.com/paper/2606.06647/) (2026)
- [Redundancy as a Structural Information Principle for Learning and Generalization](https://mlsanity.com/paper/2510.10938/) (2025)
- [DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models](https://mlsanity.com/paper/2604.15016/) (2026)
- [NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models](https://mlsanity.com/paper/2607.03925/) (2026)
- [BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understanding](https://mlsanity.com/paper/2608.04156/) (2026)
- [A Roadmap for MEG Foundation Models](https://mlsanity.com/paper/2609.04461/) (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)

## Abstract (arXiv)

Non-invasive Brain-Computer Interfaces (BCI) offer a safe and accessible means of connecting the human brain to external devices, with broad applications in home and clinical settings to enhance human capabilities. However, the high noise level and limited task-specific data in non-invasive signals constrain decoding capabilities. Recently, the adoption of self-supervised pre-training is transforming the landscape of non-invasive BCI research, enabling the development of brain foundation models to capture generic neural representations from large-scale unlabeled electroencephalography (EEG) signals with substantial noises. However, despite these advances, the field currently lacks comprehensive, practical and extensible benchmarks to assess the utility of the public foundation models across diverse BCI tasks, hindering their widespread adoption. To address this challenge, we present AdaBrain-Bench, a large-scale standardized benchmark to systematically evaluate brain foundation models in widespread non-invasive BCI tasks. AdaBrain-Bench encompasses a diverse collection of representative BCI decoding datasets spanning 7 key applications. It introduces a streamlined task adaptation pipeline integrated with multi-dimensional evaluation metrics and a set of adaptation tools. The benchmark delivers an inclusive framework for assessing generalizability of brain foundation models across key transfer settings, including cross-subject, multi-subject, and few-shot scenarios. We leverage AdaBrain-Bench to evaluate a suite of publicly available brain foundation models and offer insights into practices for selecting appropriate models in various scenarios. We make our benchmark pipeline available to enable reproducible research and external use, offering a continuously evolving platform to foster progress toward robust and generalized neural decoding solutions.