ArticleCognitive neurodynamics2024
EEGNet classification of sleep EEG for individual specialization based on data augmentation.
Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Deep Learning Approach for Classifying Rapid Eye Movement Sleep Behavior Disorder Using EEGNet.Journal of clinical neurology (Seoul, Korea) · 2026Article
- MultiScaleSleepNet: A Hybrid CNN-BiLSTM-Transformer Architecture with Multi-Scale Feature Representation for Single-Channel EEG Sleep Stage Classification.Sensors (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
- Motor imagery EEG classification via wavelet-packet synthetic augmentation and entropy-based channel selection.Frontiers in neuroscience · 2025Article
- Comparative Analysis of Single-Channel and Multi-Channel Classification of Sleep Stages Across Four Different Data Sets.Brain sciences · 2024Article
- Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis.Cognitive neurodynamics · 2024Article
- Classification of Known and Unknown Study Items in a Memory Task Using Single-Trial Event-Related Potentials and Convolutional Neural Networks.Brain sciences · 2024Article
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Authors and funding
5 authors.
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Abstract
Sleep is an essential part of human life, and the quality of one's sleep is also an important indicator of one's health. Analyzing the Electroencephalogram (EEG) signals of a person during sleep makes it possible to understand the sleep status and give relevant rest or medical advice. In this paper, a decent amount of artificial data generated with a data augmentation method based on Discrete Cosine Transform from a small amount of real experimental data of a specific individual is introduced. A classification model with an accuracy of 92.85% has been obtained. By mixing the data augmentation with the public database and training with the EEGNet, we obtained a classification model with significantly higher accuracy for the specific individual. The experiments have demonstrated that we can circumvent the subject-independent problem in sleep EEG in this way and use only a small amount of labeled data to customize a dedicated classification model with high accuracy.
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