EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation of EEG Foundation Models

• URL: http://arxiv.org/abs/2508.17742v1
• Date: Mon, 25 Aug 2025 07:34:33 GMT
• Title: EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation of EEG Foundation Models
• Authors: Wei Xiong, Jiangtong Li, Jie Li, Kun Zhu,
• Abstract summary: EEG-FM-Bench is the first comprehensive benchmark for the systematic and standardized evaluation of EEG foundation models (EEG-FMs)<n>Our contributions are threefold: (1) we curate a diverse suite of downstream tasks and datasets from canonical EEG paradigms, implementing standardized processing and evaluation protocols within a unified open-source framework; (2) we benchmark prominent state-of-the-art foundation models to establish comprehensive baseline results for a clear comparison of the current landscape; (3) we perform qualitative analyses to provide insights into model behavior and inform future architectural design.
• Score: 16.433809341013113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electroencephalography (EEG) foundation models are poised to significantly advance brain signal analysis by learning robust representations from large-scale, unlabeled datasets. However, their rapid proliferation has outpaced the development of standardized evaluation benchmarks, which complicates direct model comparisons and hinders systematic scientific progress. This fragmentation fosters scientific inefficiency and obscures genuine architectural advancements. To address this critical gap, we introduce EEG-FM-Bench, the first comprehensive benchmark for the systematic and standardized evaluation of EEG foundation models (EEG-FMs). Our contributions are threefold: (1) we curate a diverse suite of downstream tasks and datasets from canonical EEG paradigms, implementing standardized processing and evaluation protocols within a unified open-source framework; (2) we benchmark prominent state-of-the-art foundation models to establish comprehensive baseline results for a clear comparison of the current landscape; (3) we perform qualitative analyses of the learned representations to provide insights into model behavior and inform future architectural design. Through extensive experiments, we find that fine-grained spatio-temporal feature interaction, multitask unified training and neuropsychological priors would contribute to enhancing model performance and generalization capabilities. By offering a unified platform for fair comparison and reproducible research, EEG-FM-Bench seeks to catalyze progress and guide the community toward the development of more robust and generalizable EEG-FMs. Code is released at https://github.com/xw1216/EEG-FM-Bench.

Related papers

• EEG Foundation Models: Progresses, Benchmarking, and Open Problems [10.447009984769819]
  We review 50 representative EEG foundation models and organize their design choices into a unified taxonomic framework.<n>We evaluate 12 open-source foundation models and competitive specialist baselines across 13 EEG datasets spanning nine BCI paradigms.
  arXiv  Detail & Related papers  (2026-01-25T15:28:50Z)
• Foundation Models for Cross-Domain EEG Analysis Application: A Survey [11.294318502037589]
  This study presents the first comprehensive modality-oriented taxonomy for foundation models in EEG analysis.<n>We rigorously analyze each category's research ideas, theoretical foundations, and architectural innovations.<n>Our work accelerates the translation of EEG foundation models into scalable, interpretable, and online actionable solutions.
  arXiv  Detail & Related papers  (2025-08-21T16:56:28Z)
• EEG Foundation Models: A Critical Review of Current Progress and Future Directions [4.096453902709292]
  Self-supervised EEG encoders have sparked a transition towards general-purpose EEG foundation models (EEG-FMs)<n>This study reviews 10 early EEG-FMs and presents a critical synthesis of their methodology, empirical findings, and outstanding research gaps.
  arXiv  Detail & Related papers  (2025-07-15T22:52:44Z)
• CodeBrain: Towards Decoupled Interpretability and Multi-Scale Architecture for EEG Foundation Model [52.466542039411515]
  EEG foundation models (EFMs) have emerged to address the scalability issues of task-specific models.<n>We present CodeBrain, a two-stage EFM designed to fill this gap.<n>In the first stage, we introduce the TFDual-Tokenizer, which decouples heterogeneous temporal and frequency EEG signals into discrete tokens.<n>In the second stage, we propose the multi-scale EEGSSM architecture, which combines structured global convolution with sliding window attention.
  arXiv  Detail & Related papers  (2025-06-10T17:20:39Z)
• BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals [50.76802709706976]
  This paper proposes Brain Omni, the first brain foundation model that generalises across heterogeneous EEG and MEG recordings.<n>To unify diverse data sources, we introduce BrainTokenizer, the first tokenizer that quantises neural brain activity into discrete representations.<n>A total of 1,997 hours of EEG and 656 hours of MEG data are curated and standardised from publicly available sources for pretraining.
  arXiv  Detail & Related papers  (2025-05-18T14:07:14Z)
• A Survey of Model Architectures in Information Retrieval [64.75808744228067]
  We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation.<n>We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)<n>We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• Large Cognition Model: Towards Pretrained EEG Foundation Model [0.0]
  We propose a transformer-based foundation model designed to generalize across diverse EEG datasets and downstream tasks.<n>Our findings highlight the potential of pretrained EEG foundation models to accelerate advancements in neuroscience, personalized medicine, and BCI technology.
  arXiv  Detail & Related papers  (2025-02-11T04:28:10Z)
• SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation [81.36747103102459]
  Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications.<n>Current state-of-the-art methods focus on training innovative architectural designs on confined datasets.<n>We investigate the impact of scaling up EHPS towards a family of generalist foundation models.
  arXiv  Detail & Related papers  (2025-01-16T18:59:46Z)
• GEFM: Graph-Enhanced EEG Foundation Model [16.335330142000657]
  Foundation models offer a promising solution by leveraging large-scale unlabeled data through pre-training.<n>We propose Graph-Enhanced EEG Foundation Model (GEFM), a novel foundation model for EEG that integrates both temporal and inter-channel information.<n>Our architecture combines Graph Neural Networks (GNNs), which effectively capture relational structures, with a masked autoencoder to enable efficient pre-training.
  arXiv  Detail & Related papers  (2024-11-29T06:57:50Z)
• GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [56.63218531256961]
  We introduce GenBench, a benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance.
  arXiv  Detail & Related papers  (2024-06-01T08:01:05Z)
• Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions [48.0590120095748]
  We propose a novel neural architecture search (NAS) framework named AutoFM, which can automatically search for the optimal model architectures for encoding diverse input modalities and fusion strategies. We conduct thorough experiments on real-world multi-modal EHR data and prediction tasks, and the results demonstrate that our framework achieves significant performance improvement over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-20T15:14:14Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.