EEGNet classification of sleep EEG for individual specialization based on data augmentation

Xia, Mo; Zhao, Xuyang; Deng, Rui; Lu, Zheng; Cao, Jianting

doi:10.1007/s11571-023-10062-0

Cogn Neurodyn

2024

DOI: 10.1007/s11571-023-10062-0

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EEGNet classification of sleep EEG for individual specialization based on data augmentation

Mo Xia,

Xuyang Zhao,

Rui Deng

et al.

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2024

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Cited by 3 publications

References 30 publications

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Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis

Li,

Liu,

Zhang

et al. 2024

Cogn Neurodyn

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Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis

Li,

Liu,

Zhang

et al. 2024

Cogn Neurodyn

View full text Add to dashboard Cite

Classification of Known and Unknown Study Items in a Memory Task Using Single-Trial Event-Related Potentials and Convolutional Neural Networks

Delgado-Munoz,

Matsunaka,

Hiraki

2024

Brain Sciences

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This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and the identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 62% for known items and 66% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.

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Comparative Analysis of Single-Channel and Multi-Channel Classification of Sleep Stages Across Four Different Data Sets

Zhang,

He,

Shang

et al. 2024

Brain Sciences

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Background: Manually labeling sleep stages is time-consuming and labor-intensive, making automatic sleep staging methods crucial for practical sleep monitoring. While both single- and multi-channel data are commonly used in automatic sleep staging, limited research has adequately investigated the differences in their effectiveness. Methods: In this study, four public data sets—Sleep-SC, APPLES, SHHS1, and MrOS1—are utilized, and an advanced hybrid attention neural network composed of a multi-branch convolutional neural network and the multi-head attention mechanism is employed for automatic sleep staging. Results: The experimental results show that, for sleep staging using 2–5 classes, a combination of single-channel electroencephalography (EEG) and dual-channel electrooculography (EOG) consistently outperforms single-channel EEG with single-channel EOG, which in turn outperforms single-channel EEG or single-channel EOG alone. For instance, for five-class sleep staging using the MrOS1 data set, the combination of single-channel EEG and dual-channel EOG resulted in an accuracy of 87.18%, whereas the combination of single-channel EEG and single-channel EOG yielded an accuracy of 85.77%. In comparison, single-channel EEG alone achieved an accuracy of 85.25% and single-channel EOG alone achieved an accuracy of 83.66%. Conclusions: This study highlights the significance of combining EEG and EOG signals in automatic sleep staging, while also providing valuable insights for the channel design of portable sleep monitoring devices.

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EEGNet classification of sleep EEG for individual specialization based on data augmentation

Cited by 3 publications

References 30 publications

Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis

Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis

Classification of Known and Unknown Study Items in a Memory Task Using Single-Trial Event-Related Potentials and Convolutional Neural Networks

Comparative Analysis of Single-Channel and Multi-Channel Classification of Sleep Stages Across Four Different Data Sets

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