mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup

• URL: http://arxiv.org/abs/2504.07987v1
• Date: Mon, 07 Apr 2025 06:24:23 GMT
• Title: mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup
• Authors: Xuan-Hao Liu, Bao-Liang Lu, Wei-Long Zheng,
• Abstract summary: Cross-subject electroencephalography (EEG) classification exhibits great challenges due to the diversity of cognitive processes and physiological structures between different subjects.<n>Privacy concerns associated with EEG pose significant limitations to data sharing between different hospitals and institutions.<n> Federated learning (FL) enables multiple decentralized clients to collaboratively train a global model without direct communication of raw data.
• Score: 5.367329958716485
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The cross-subject electroencephalography (EEG) classification exhibits great challenges due to the diversity of cognitive processes and physiological structures between different subjects. Modern EEG models are based on neural networks, demanding a large amount of data to achieve high performance and generalizability. However, privacy concerns associated with EEG pose significant limitations to data sharing between different hospitals and institutions, resulting in the lack of large dataset for most EEG tasks. Federated learning (FL) enables multiple decentralized clients to collaboratively train a global model without direct communication of raw data, thus preserving privacy. For the first time, we investigate the cross-subject EEG classification in the FL setting. In this paper, we propose a simple yet effective framework termed mixEEG. Specifically, we tailor the vanilla mixup considering the unique properties of the EEG modality. mixEEG shares the unlabeled averaged data of the unseen subject rather than simply sharing raw data under the domain adaptation setting, thus better preserving privacy and offering an averaged label as pseudo-label. Extensive experiments are conducted on an epilepsy detection and an emotion recognition dataset. The experimental result demonstrates that our mixEEG enhances the transferability of global model for cross-subject EEG classification consistently across different datasets and model architectures. Code is published at: https://github.com/XuanhaoLiu/mixEEG.

Related papers

• CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information [61.1904164368732]
  We propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals.<n>Specifically, CognitionCapturer trains Modality Experts for each modality to extract cross-modal information from the EEG modality.<n>The framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities.
  arXiv  Detail & Related papers  (2024-12-13T16:27:54Z)
• RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier [0.0]
  Intracranial electroencephalography (iEEG) is increasingly used for clinical and brain-computer interface applications. We introduce a novel decoder model that is robust to inter-subject electrode implantation variability. We call this model RISE-iEEG, which stands for Robust to Inter-Subject Electrode Implantation Variability iEEG.
  arXiv  Detail & Related papers  (2024-08-12T18:33:19Z)
• Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets [53.367212596352324]
  We propose an unsupervised approach leveraging EEG signal physics. We map EEG channels to fixed positions using field, source-free domain adaptation. Our method demonstrates robust performance in brain-computer interface (BCI) tasks and potential biomarker applications.
  arXiv  Detail & Related papers  (2024-03-07T16:17:33Z)
• hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications [3.031375888004876]
  Two main issues challenge the existing DL-based modeling methods for EEG. High variability between subjects and low signal-to-noise ratio make it difficult to ensure a good quality in the EEG data. We propose two variational autoencoder models, namely vEEGNet-ver3 and hvEEGNet, to target the problem of high-fidelity EEG reconstruction.
  arXiv  Detail & Related papers  (2023-11-20T15:36:31Z)
• EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognition [7.1695247553867345]
  We propose a novel semi-supervised learning framework (EEGMatch) to leverage both labeled and unlabeled EEG data. Extensive experiments are conducted on two benchmark databases (SEED and SEED-IV)
  arXiv  Detail & Related papers  (2023-03-27T12:02:33Z)
• Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface [4.8276709243429]
  Cross-subject application of EEG-based brain-computer interface (BCI) has always been limited by large individual difference and complex characteristics that are difficult to perceive. We propose a cross-subject EEG classification framework with a generative adversarial networks (GANs) based method named common spatial GAN (CS-GAN) Our framework provides a promising way to deal with the cross-subject problem and promote the practical application of BCI.
  arXiv  Detail & Related papers  (2021-02-08T10:37:03Z)
• EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification [123.93460670568554]
  This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based motor imagery (MI) classification. The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network. The proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing.
  arXiv  Detail & Related papers  (2021-01-24T19:03:10Z)
• A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition [51.203579838210885]
  We propose a transferable attention neural network (TANN) for EEG emotion recognition. TANN learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator.
  arXiv  Detail & Related papers  (2020-09-21T02:42:30Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• Federated Transfer Learning for EEG Signal Classification [14.892851587424936]
  We propose a privacy-preserving deep learning architecture named federated transfer learning (FTL) for EEG classification. FTL approach achieves 2% higher classification accuracy in a subject-adaptive analysis. In the absence of multi-subject data, our architecture provides 6% better accuracy compared to other state-of-the-art DL architectures.
  arXiv  Detail & Related papers  (2020-04-26T09:03:19Z)
• Repulsive Mixture Models of Exponential Family PCA for Clustering [127.90219303669006]
  The mixture extension of exponential family principal component analysis ( EPCA) was designed to encode much more structural information about data distribution than the traditional EPCA. The traditional mixture of local EPCAs has the problem of model redundancy, i.e., overlaps among mixing components, which may cause ambiguity for data clustering. In this paper, a repulsiveness-encouraging prior is introduced among mixing components and a diversified EPCA mixture (DEPCAM) model is developed in the Bayesian framework.
  arXiv  Detail & Related papers  (2020-04-07T04:07:29Z)

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.