Data Normalization Strategies for EEG Deep Learning

• URL: http://arxiv.org/abs/2506.22455v1
• Date: Sun, 15 Jun 2025 15:33:41 GMT
• Title: Data Normalization Strategies for EEG Deep Learning
• Authors: Dung Truong, Arnaud Delorme,
• Abstract summary: We show that optimal normalization strategies differ significantly between training paradigms.<n>Window-level within-channel normalization yields the best performance in supervised tasks.<n>Our findings challenge the assumption that a universal normalization strategy can generalize across learning settings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Normalization is a critical yet often overlooked component in the preprocessing pipeline for EEG deep learning applications. The rise of large-scale pretraining paradigms such as self-supervised learning (SSL) introduces a new set of tasks whose nature is substantially different from supervised training common in EEG deep learning applications. This raises new questions about optimal normalization strategies for the applicable task. In this study, we systematically evaluate the impact of normalization granularity (recording vs. window level) and scope (cross-channel vs. within-channel) on both supervised (age and gender prediction) and self-supervised (Contrastive Predictive Coding) tasks. Using high-density resting-state EEG from 2,836 subjects in the Healthy Brain Network dataset, we show that optimal normalization strategies differ significantly between training paradigms. Window-level within-channel normalization yields the best performance in supervised tasks, while minimal or cross-channel normalization at the window level is more effective for SSL. These results underscore the necessity of task-specific normalization choices and challenge the assumption that a universal normalization strategy can generalize across learning settings. Our findings provide practical insights for developing robust EEG deep learning pipelines as the field shifts toward large-scale, foundation model training.

Related papers

• Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning [104.27224674122313]
  Fine-tuning MLLM has become a common practice to improve performance on specific downstream tasks. To balance the trade-off between generalization and specialization, we propose measuring the parameter importance for both pre-trained and fine-tuning distributions.
  arXiv  Detail & Related papers  (2024-11-17T01:16:37Z)
• NormAUG: Normalization-guided Augmentation for Domain Generalization [60.159546669021346]
  We propose a simple yet effective method called NormAUG (Normalization-guided Augmentation) for deep learning. Our method introduces diverse information at the feature level and improves the generalization of the main path. In the test stage, we leverage an ensemble strategy to combine the predictions from the auxiliary path of our model, further boosting performance.
  arXiv  Detail & Related papers  (2023-07-25T13:35:45Z)
• PEAR: Primitive Enabled Adaptive Relabeling for Boosting Hierarchical Reinforcement Learning [25.84621883831624]
  Hierarchical reinforcement learning (HRL) has the potential to solve complex long horizon tasks using temporal abstraction and increased exploration.<n>We present primitive enabled adaptive relabeling (PEAR)<n>We first perform adaptive relabeling on a few expert demonstrations to generate efficient subgoal supervision.<n>We then jointly optimize HRL agents by employing reinforcement learning (RL) and imitation learning (IL)
  arXiv  Detail & Related papers  (2023-06-10T09:41:30Z)
• Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice [54.03076395748459]
  A central question in the meta-learning literature is how to regularize to ensure generalization to unseen tasks. We present a generalization bound for meta-learning, which was first derived by Rothfuss et al. We provide a theoretical analysis and empirical case study under which conditions and to what extent these guarantees for meta-learning improve upon PAC-Bayesian per-task learning bounds.
  arXiv  Detail & Related papers  (2022-11-14T08:51:04Z)
• Subquadratic Overparameterization for Shallow Neural Networks [60.721751363271146]
  We provide an analytical framework that allows us to adopt standard neural training strategies. We achieve the desiderata viaak-Lojasiewicz, smoothness, and standard assumptions.
  arXiv  Detail & Related papers  (2021-11-02T20:24:01Z)
• Normalization Techniques in Training DNNs: Methodology, Analysis and Application [111.82265258916397]
  Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs) This paper reviews and comments on the past, present and future of normalization methods in the context of training.
  arXiv  Detail & Related papers  (2020-09-27T13:06:52Z)
• Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning [90.93035276307239]
  We propose an information theoretic regularization objective and an annealing-based optimization method to achieve better generalization ability in RL agents. We demonstrate the extreme generalization benefits of our approach in different domains ranging from maze navigation to robotic tasks. This work provides a principled way to improve generalization in RL by gradually removing information that is redundant for task-solving.
  arXiv  Detail & Related papers  (2020-08-03T02:24:20Z)
• Exemplar Normalization for Learning Deep Representation [34.42934843556172]
  This work investigates a novel dynamic learning-to-normalize (L2N) problem by proposing Exemplar Normalization (EN) EN is able to learn different normalization methods for different convolutional layers and image samples of a deep network.
  arXiv  Detail & Related papers  (2020-03-19T13:23:40Z)

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.