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EEG Sequence Dependencies Learning With Fine-grained Sleep Transition Graph for Sleep Stage Classification

EEG Sequence Dependencies Learning With Fine-grained Sleep T...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liang, Jingxin Philip, C.L. Xu, Yuebin Zhang, Tong South China University of Technology School of Computer Science&Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China

ISBN: (纸本)9798350386226

Sleep stage classification assists in evaluating sleep quality and diagnosing sleep disorders. Existing methods based on spatial-temporal graph convolutional networks (STGCNs) have shown excellent performance in recognizing sleep stages. However, current works learn transition dependencies in sleep sequence in a coarse manner and restrict the modeling of brain activity to a significantly brief period. To address the above issues, we propose a fine-grained sleep transition graph (FSTG) method to learn EEG sequence dependencies for sleep stage classification in this paper. Specifically, the novel sleep transition graph (STG) constructor (STG-C) is proposed to model sleep sequence transition dependencies in a more fine-grained manner, extracting effective auxiliary information from sleep contextual sequences for sleep staging. In the STG-C, we initially explore brain connectivity over extended sleep temporal scales, utilizing the graph fusion mechanism (GFM) to learn the multiple temporal correlations of brain channels. Furthermore, to better capture dependencies of sleep sequence, a skip-gated STGCN (SG-STGCN) is designed as the strong STG sequence learner. The proposed FSTG model achieves state-of-the-art performance in public sleep staging datasets, ISRUC-S3 and ISRUC-S1. Additionally, visualization experiments demonstrate the effectiveness and interpretability of FSTG in sleep staging classification. © 2024 IEEE.

关键词： Sleep research

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Enhancing Generalized EEG Classification with Decomposed Statistics-diverse Feature Augmentation

Enhancing Generalized EEG Classification with Decomposed Sta...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： He, Yubin Chen, C.L. Philip Chen, Bianna Zhang, Tong School of Computer Science&Engineering South China University of Technology Guangdong Guangzhou510006 China Pazhou Lab Guangdong Guangzhou510335 China

ISBN: (纸本)9798350368741

Learning a generalized EEG representation under limited data and subject variability is a long-standing challenge. Most studies utilized data augmentation to extend the distribution of training data, which may hinder the diversity of augmented samples to cover more subject variability. In this paper, we propose a decomposed statistics-diverse feature augmentation (DSFA) framework for generalized EEG learning. The wavelet-based decomposed representation module decomposes signals into approximation and detail features, thus deriving semantics from original signals into approximation features to avoid over-transformation. The statistics-diverse feature augmentation module augments features to extend beyond the original feature space by manipulating the statistics of features. Extensive experiments on three datasets demonstrate that our approach achieves state-of-the-art performance in different classification tasks. Our repository is public at https://***/1940653868/DSFA. © 2025 IEEE.

关键词： EEG Classification Feature Augmentation Model Generalization Signal Decomposition Transformer

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Enhancing Generalized EEG Classification with Decomposed Statistics-diverse Feature Augmentation

Enhancing Generalized EEG Classification with Decomposed Sta...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yubin He C.L. Philip Chen Bianna Chen Tong Zhang School of Computer Science&Engineering South China University of Technology Guangzhou China Pazhou Lab Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Adaptation models Semantics Training data Signal processing Brain modeling Data augmentation Electroencephalography Acoustics Speech processing

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EEG Sequence Dependencies Learning With Fine-grained Sleep Transition Graph for Sleep Stage Classification

EEG Sequence Dependencies Learning With Fine-grained Sleep T...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jingxin Liang C.L. Philip Yuebin Xu Tong Zhang School of Computer Science&Engineering South China University of Technology Guangzhou China Pazhou Lab Guangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

关键词： Visualization Correlation Sleep Graph convolutional networks Biological system modeling Brain modeling Electroencephalography Data mining Bioinformatics Context modeling

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Cross-Subject EEG Emotion Recognition Using Domain Adaptive Few-Shot Learning Networks

Cross-Subject EEG Emotion Recognition Using Domain Adaptive ...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Ning, Run Philip Chen, C.L. Zhang, Tong Sch. of Computer Science Engineering South China University of Technology Guangzhou China Sch. of Computer Science Engineering South China University of Technology Pazhou Lab Guangzhou China

ISBN: (纸本)9781665401265

Due to the individual differences and nonstationary of EEG signals, it is difficult to classify EEG emotions with traditional machine methods, which assume that the training and testing set come from the same data distribution, but this assumption is usually not true in the EEG field, therefore the accuracy of emotion recognition is very poor. In this paper, a Single-Source Domain Adaptive Few-Shot Learning Networks (SDA-FSL) was proposed for cross-subject EEG emotion recognition. This is the first time that domain adaptation method with few-shot learning has been used in the field of EEG emotion recognition. A CBAM-based feature mapping module was designed to extract the common features of the two domains, and the domain adaptation module was used to align the data distribution of two domains. In addition, Prototypical Networks with instance-attention mechanism is introduced to preserve domain-specific information. The proposed method was evaluated on DEAP and SEED datasets in within-dataset and cross-dataset experiments under various N-way k-shot settings. Experimental results show that the performance of SDA-FSL outperforms other comparison methods and has superior generalization performance on cross-dataset experiments. © 2021 IEEE.

关键词： Emotion Recognition

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Cross-Subject EEG Emotion Recognition Using Domain Adaptive Few-Shot Learning Networks

Cross-Subject EEG Emotion Recognition Using Domain Adaptive ...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Run Ning C.L. Philip Chen Tong Zhang Sch. of Computer Science&Engineering South China University of Technology Guangzhou China Sch. of Computer Science&Engineering South China University of Technology Pazhou Lab Guangzhou China

ISBN: (纸本)9781665429825

关键词： Training Electrodes Emotion recognition Adaptive systems Conferences Feature extraction Electroencephalography

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