mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup
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.
Code (1)
Tasks
Domain AdaptationEEGEmotion RecognitionFederated LearningPseudo LabelMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A collaborative ensemble construction method for federated random forest
Random forests are considered a cornerstone in machine learning for their robustness and versatility. Despite these strengths, their conventional centralized training is ill-suited for the modern landscape of data that i…
Federated LearningFederated Transfer Learning for EEG Signal Classification
The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets. Privacy concerns as…
ClassificationDomain AdaptationEEGEEG Signal Classification+6TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic
Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy. Despite its widespread adoption, most FL approaches focusing solely on privacy protection fall short …
Decision MakingFederated LearningPersonalized Federated LearningFederated Learning for Medical Image Classification: A Comprehensive Benchmark
The federated learning paradigm is wellsuited for the field of medical image analysis, as it can effectively cope with machine learning on isolated multicenter data while protecting the privacy of participating parties. …
Computational EfficiencyDenoisingFederated Learningimage-classification+3ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks
Cross-subject variability in EEG degrades performance of current deep learning models, limiting the development of brain-computer interface (BCI). This paper proposes ISAM-MTL, which is a multi-task learning (MTL) EEG cl…
Brain Computer InterfaceClassificationEEGFew-Shot Learning+2