Improving Sleep Stage Classification Performance by Single-Channel EEG Data Augmentation via Spectral Band Blending

Lee, Choel-Hui; Kim, Hyunji; Heo, Jae-Wook; Kim, Hakseung; Kim, Dong-Joo

doi:10.1109/bci51272.2021.9385297

Cited by 7 publications

(2 citation statements)

References 1 publication

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“…The use of single-channel EEG for automated sleep staging has received much attention in the literature. Specifically, deep learning-based techniques (Sors et al, 2018 ; Kuo and Chen, 2020 ; Fan et al, 2021 ; Lee et al, 2021 ) have made significant progress. These approaches create various network structures to extract characteristics from EEG data and capture temporal dependencies.…”

Section: Methodsmentioning

confidence: 99%

Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention

Gao

Wang

et al. 2023

Front. Neurosci.

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The diagnosis and management of sleep problems depend heavily on sleep staging. For autonomous sleep staging, many data-driven deep learning models have been presented by trying to construct a large-labeled auxiliary sleep dataset and test it by electroencephalograms on different subjects. These approaches suffer a significant setback cause it assumes the training and test data come from the same or similar distribution. However, this is almost impossible in scenario cross-dataset due to inherent domain shift between domains. Unsupervised domain adaption was recently created to address the domain shift issue. However, only a few customized UDA solutions for sleep staging due to two limitations in previous UDA methods. First, the domain classifier does not consider boundaries between classes. Second, they depend on a shared model to align the domain that could miss the information of domains when extracting features. Given those restrictions, we present a novel UDA approach that combines category decision boundaries and domain discriminator to align the distributions of source and target domains. Also, to keep the domain-specific features, we create an unshared attention method. In addition, we investigated effective data augmentation in cross-dataset sleep scenarios. The experimental results on three datasets validate the efficacy of our approach and show that the proposed method is superior to state-of-the-art UDA methods on accuracy and MF1-Score.

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Section: Methodsmentioning

confidence: 99%

Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention

Gao

Wang

et al. 2023

Front. Neurosci.

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“…Brain-computer interface (BCI) is a research topic about how to interpret and utilize brain activities of humans using computer systems and it has been actively studied due to the wide range of applications such as human intention recognition [1]- [3] and sleep stage classification [4]- [7]. Specifically, researchers have devoted a lot of efforts to decode the human mind from brain signals [8], [9] For instance, some work allows users to control BCI-related devices like prosthetic hands by reading the users' intentions from brain signals [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Subject Adaptive EEG-Based Visual Recognition

Lee

Hwang²,

Jeon

et al. 2022

Lecture Notes in Computer Science

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This paper focuses on subject adaptation for EEGbased visual recognition. It aims at building a visual stimuli recognition system customized for the target subject whose EEG samples are limited, by transferring knowledge from abundant data of source subjects. Existing approaches consider the scenario that samples of source subjects are accessible during training. However, it is often infeasible and problematic to access personal biological data like EEG signals due to privacy issues. In this paper, we introduce a novel and practical problem setup, namely source-free subject adaptation, where the source subject data are unavailable and only the pre-trained model parameters are provided for subject adaptation. To tackle this challenging problem, we propose classifier-based data generation to simulate EEG samples from source subjects using classifier responses. Using the generated samples and target subject data, we perform subjectindependent feature learning to exploit the common knowledge shared across different subjects. Notably, our framework is generalizable and can adopt any subject-independent learning method. In the experiments on the EEG-ImageNet40 benchmark, our model brings consistent improvements regardless of the choice of subject-independent learning. Also, our method shows promising performance, recording top-1 test accuracy of 74.6% under the 5-shot setting even without relying on source data. Our code can be found at https://github.com/DeepBCI/Deep-BCI.

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Automatic Sleep-Wake Scoring with Optimally Selected EEG Channels from High-Density EEG

Herleiksplass,

Moctezuma,

Furuki

et al. 2023

Lecture Notes in Computer Science

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Improving Sleep Stage Classification Performance by Single-Channel EEG Data Augmentation via Spectral Band Blending

Cited by 7 publications

References 1 publication

Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention

Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention

Subject Adaptive EEG-Based Visual Recognition

Automatic Sleep-Wake Scoring with Optimally Selected EEG Channels from High-Density EEG

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