EEG-D3: A Solution to the Hidden Overfitting Problem of Deep Learning Models

• URL: http://arxiv.org/abs/2512.13806v1
• Date: Mon, 15 Dec 2025 19:00:10 GMT
• Title: EEG-D3: A Solution to the Hidden Overfitting Problem of Deep Learning Models
• Authors: Siegfried Ludwig, Stylianos Bakas, Konstantinos Barmpas, Georgios Zoumpourlis, Dimitrios A. Adamos, Nikolaos Laskaris, Yannis Panagakis, Stefanos Zafeiriou,
• Abstract summary: We introduce Disentangled Decoding Decomposition (D3), a weakly supervised method for training deep learning models across EEG datasets.<n>By predicting the place in the respective trial sequence from which the input window was sampled, EEG-D3 separates latent components of brain activity.<n>We exploit the linearly separable latent space for effective few-shot learning on sleep stage classification.
• Score: 33.22173262655432
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep learning for decoding EEG signals has gained traction, with many claims to state-of-the-art accuracy. However, despite the convincing benchmark performance, successful translation to real applications is limited. The frequent disconnect between performance on controlled BCI benchmarks and its lack of generalisation to practical settings indicates hidden overfitting problems. We introduce Disentangled Decoding Decomposition (D3), a weakly supervised method for training deep learning models across EEG datasets. By predicting the place in the respective trial sequence from which the input window was sampled, EEG-D3 separates latent components of brain activity, akin to non-linear ICA. We utilise a novel model architecture with fully independent sub-networks for strict interpretability. We outline a feature interpretation paradigm to contrast the component activation profiles on different datasets and inspect the associated temporal and spatial filters. The proposed method reliably separates latent components of brain activity on motor imagery data. Training downstream classifiers on an appropriate subset of these components prevents hidden overfitting caused by task-correlated artefacts, which severely affects end-to-end classifiers. We further exploit the linearly separable latent space for effective few-shot learning on sleep stage classification. The ability to distinguish genuine components of brain activity from spurious features results in models that avoid the hidden overfitting problem and generalise well to real-world applications, while requiring only minimal labelled data. With interest to the neuroscience community, the proposed method gives researchers a tool to separate individual brain processes and potentially even uncover heretofore unknown dynamics.

Related papers

• DIS2: Disentanglement Meets Distillation with Classwise Attention for Robust Remote Sensing Segmentation under Missing Modalities [28.992992584085787]
  DIS2 is a new paradigm shifting from modality-shared feature dependence to active, guided missing features compensation.<n> Compensatory features are explicitly captured which, when fused with the features of the available modality, approximate the ideal fused representation of the full-modality case.<n>Our proposed approach significantly outperforms state-of-the-art methods across benchmarks.
  arXiv  Detail & Related papers  (2026-01-20T01:33:54Z)
• Towards a more realistic evaluation of machine learning models for bearing fault diagnosis [0.28873930745906956]
  This paper investigates the issue of data leakage in vibration-based bearing fault diagnosis and its impact on model evaluation.<n>We propose a leakage-free evaluation methodology centered on bearing-wise data partitioning, ensuring no overlap between the physical components used for training and testing.<n>We evaluate our methodology on three widely adopted datasets: CWRU, Paderborn University (PU), and University of Ottawa (UORED-VAF)
  arXiv  Detail & Related papers  (2025-09-26T12:35:02Z)
• P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation [60.08541107831459]
  We propose a progressive and periodic perturbation mechanism (P3M) and a boundary-focused loss to guide the learning of unlabeled data.<n>Our method achieves state-of-the-art performance on two 2D and 3D datasets.
  arXiv  Detail & Related papers  (2025-05-21T05:35:28Z)
• M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition [11.252832459891566]
  We propose Manifold-based Domain Adaptation with Dynamic Distribution (M3D), a lightweight, non-deep transfer learning framework.<n>M3D consists of four key modules: manifold feature transformation, dynamic distribution alignment, classifier learning, and ensemble learning.<n> Experimental results show that M3D outperforms traditional non-deep learning methods with a 4.47% average improvement.
  arXiv  Detail & Related papers  (2024-04-24T03:08:25Z)
• FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models [59.13757801286343]
  Few-shot class-incremental learning aims to mitigate the catastrophic forgetting issue when a model is incrementally trained on limited data.<n>We introduce the FILP-3D framework with two novel components: the Redundant Feature Eliminator (RFE) for feature space misalignment and the Spatial Noise Compensator (SNC) for significant noise.
  arXiv  Detail & Related papers  (2023-12-28T14:52:07Z)
• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection [37.99031842449251]
  Video anomaly detection under weak supervision presents significant challenges. We present a weakly supervised anomaly detection framework that focuses on efficient context modeling and enhanced semantic discriminability. Our approach significantly improves the detection accuracy of certain anomaly sub-classes, underscoring its practical value and efficacy.
  arXiv  Detail & Related papers  (2023-06-26T06:45:16Z)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface [17.85507707727557]
  We show that factorizing EEG signals allows the model to extract rich and decisive features under sparse condition. Evaluations were conducted on a single-arm motor imagery dataset.
  arXiv  Detail & Related papers  (2022-06-17T00:30:43Z)
• Neurosymbolic hybrid approach to driver collision warning [64.02492460600905]
  There are two main algorithmic approaches to autonomous driving systems. Deep learning alone has achieved state-of-the-art results in many areas. But sometimes it can be very difficult to debug if the deep learning model doesn't work.
  arXiv  Detail & Related papers  (2022-03-28T20:29:50Z)
• The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection [62.1185839286255]
  Low-cost monocular 3D object detection plays a fundamental role in autonomous driving. We introduce a Dynamic Feature Reflecting Network, named DFR-Net. We rank 1st among all the monocular 3D object detectors in the KITTI test set.
  arXiv  Detail & Related papers  (2021-12-28T07:31:18Z)

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.