Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models

• URL: http://arxiv.org/abs/2605.17562v1
• Date: Sun, 17 May 2026 17:47:00 GMT
• Title: Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models
• Abstract summary: EEG foundation models (EEG-FMs) have been evaluated predominantly on clean, in-distribution accuracy.<n>This study addresses these gaps by benchmarking six EEG-FMs against a baseline deep learning model across eight datasets.
• Score: 31.542508691296053
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: EEG foundation models (EEG-FMs) have been evaluated predominantly on clean, in-distribution accuracy, leaving their robustness, interpretability and representational quality largely unexamined. This study addresses these gaps by benchmarking six EEG-FMs against a baseline deep learning model across eight datasets. Beyond clean accuracy, we conduct three layers of analysis: (i) Robustness: we apply test-time perturbations including additive noise, random and region-based channel dropout and region-specific noise injection. Our analyses show that no single model dominates all failure modes. The most noise-robust model is among the most fragile under channel dropout and much of the dropout fragility disappears when channels are removed rather than zero-padded. (ii) Interpretability: we present the first application of Attention-Aware Layer-Wise Relevance Propagation (AttnLRP) to EEG-FMs and show that models broadly concentrate relevance on task-appropriate brain regions consistent with known neurophysiology. However, attribution maps remain spatially stable under perturbation while predictions degrade, suggesting that the models attend to the correct brain regions but decode corrupted content. (iii) Expressiveness: With block-wise probing we show that late blocks are repurposed during fine-tuning, while early blocks already hold task-related information. Furthermore, we demonstrate that the poor head-only performance previously attributed to low-quality pre-trained representations is largely explained by pooling and that EEG-FMs possess sufficient representational capacity when their token-level embeddings are preserved. Together, these findings provide the first systematic assessment of robustness, interpretability and expressiveness for EEG-FMs and highlight critical considerations for their development.

Related papers

• The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models [0.1274452325287335]
  We benchmarked twelve pathology foundation models (PFMs) and ResNet baselines.<n>PFMs consistently outperform CNNs in both robustness and domain generalisation, yet model scaling shows diminishing returns.<n> NR-Kfold analysis reveals systematic accuracy loss and increased variability when training-test similarity is broken.
  arXiv  Detail & Related papers  (2026-07-05T16:49:09Z)
• Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models [56.29123284262618]
  This work proposes leveraging the universal representation capabilities of visual foundation models to estimate inherent data uncertainty.<n>We analyze the feature diversity of the model's decoded representations and quantify their singular value energy to define the semantic perception scale for each class.<n>Based on this foundation, we design two uncertainty-driven application strategies: (1) the aleatoric uncertainty-aware data filtering mechanism to eliminate potentially noisy samples and enhance model learning quality; and (2) the dynamic uncertainty-aware optimization strategy that adaptively adjusts class-specific loss weights during training based on the semantic perception scale, combined with a label denoising mechanism to improve training stability
  arXiv  Detail & Related papers  (2026-04-13T03:59:54Z)
• Noisy Analysis of Quantum SMOTE on Condition Monitoring and Fault Classification in Industrial and Energy Systems [0.5505634045241289]
  Imbalanced machine learning models are a fundamental issue in industrial condition monitoring and fault classification pipelines.<n>This work presents a detailed benchmarking and investigation of classical classifiers under class imbalance mitigation.<n>The results show that QSMOTE consistently corrects distributional skew and significantly enhances the performance of non-linear classifiers.
  arXiv  Detail & Related papers  (2026-01-16T16:44:38Z)
• Lost in the Noise: How Reasoning Models Fail with Contextual Distractors [57.31788955167306]
  Recent advances in reasoning models and agentic AI systems have led to an increased reliance on diverse external information.<n>We introduce NoisyBench, a comprehensive benchmark that systematically evaluates model robustness across 11 datasets in RAG, reasoning, alignment, and tool-use tasks.<n>Our evaluation reveals a catastrophic performance drop of up to 80% in state-of-the-art models when faced with contextual distractors.
  arXiv  Detail & Related papers  (2026-01-12T05:43:51Z)
• A Context-Aware Temporal Modeling through Unified Multi-Scale Temporal Encoding and Hierarchical Sequence Learning for Single-Channel EEG Sleep Staging [0.0]
  This study focuses on single-channel electroencephalography (EEG), a practical and widely available signal for automatic sleep staging.<n>Existing approaches face challenges such as class imbalance, limited receptive-field modeling, and insufficient interpretability.<n>This work proposes a context-aware and interpretable framework for single-channel EEG sleep staging.
  arXiv  Detail & Related papers  (2025-12-28T15:42:33Z)
• Did Models Sufficient Learn? Attribution-Guided Training via Subset-Selected Counterfactual Augmentation [61.248535801314375]
  Subset-Selected Counterfactual Augmentation (SS-CA)<n>We develop Counterfactual LIMA to identify minimal spatial region sets whose removal can selectively alter model predictions.<n>Experiments show that SS-CA improves generalization on in-distribution (ID) test data and achieves superior performance on out-of-distribution (OOD) benchmarks.
  arXiv  Detail & Related papers  (2025-11-15T08:39:22Z)
• Learning Robust Diffusion Models from Imprecise Supervision [75.53546939251146]
  DMIS is a unified framework for training robust Conditional Diffusion Models from Imprecise Supervision.<n>Our framework is derived from likelihood and decomposes the objective into generative and classification components.<n>Experiments on diverse forms of imprecise supervision, covering tasks covering image generation, weakly supervised learning, and dataset condensation demonstrate that DMIS consistently produces high-quality and class-discriminative samples.
  arXiv  Detail & Related papers  (2025-10-03T14:00:32Z)
• Deep Feedback Models [0.9310318514564272]
  Deep Feedback Models (DFMs) are a new class of stateful neural networks that combine bottom up input with high level representations over time.<n>This feedback mechanism introduces dynamics into otherwise static architectures, enabling DFMs to iteratively refine their internal state.<n>We measure DFMs under two key conditions: robustness to noise and generalization with limited data.
  arXiv  Detail & Related papers  (2025-09-19T12:03:18Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models [53.67562579184457]
  This paper focuses on probabilistic STG forecasting, which is challenging due to the difficulty in modeling uncertainties and complex dependencies. We present the first attempt to generalize the popular denoising diffusion models to STGs, leading to a novel non-autoregressive framework called DiffSTG. Our approach combines the intrinsic-temporal learning capabilities STNNs with the uncertainty measurements of diffusion models.
  arXiv  Detail & Related papers  (2023-01-31T13:42:36Z)
• Pre-training via Denoising for Molecular Property Prediction [53.409242538744444]
  We describe a pre-training technique that utilizes large datasets of 3D molecular structures at equilibrium. Inspired by recent advances in noise regularization, our pre-training objective is based on denoising.
  arXiv  Detail & Related papers  (2022-05-31T22:28:34Z)

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.