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

• Published in: Cognitive neurodynamics Vol. 18; no. 4; pp. 1539 - 1547
• Main Authors: Xia, Mo, Zhao, Xuyang, Deng, Rui, Lu, Zheng, Cao, Jianting
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2024; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Biochemistry; Biomedical and Life Sciences; Biomedicine; Classification; Cognitive Psychology; Computer Science; Data analysis; Data augmentation; Discrete cosine transform; EEG; Electroencephalography; Eye movements; Medical research; Neurosciences; Research Article; Signal classification; Sleep; Sleep health; Data augmentation; EEGNet; EEG
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-023-10062-0

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.