Finding Robust Low Dimensional Features for Sleep Detection Using EEG Data

The study of sleep is crucial for understanding how our bodies function., and electroencephalogram (EEG) offers a convenient way to examine sleep. Sleep can be categorized into wakefulness., rapid eye movement (REM) sleep., and stages 1-4, with 4 being the deepest stage of sleep. We strive to study...

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Veröffentlicht in:	2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA) S. 42 - 45
Hauptverfasser:	Mao, Helen X., Widjaja, Joseph, Guo, Yifan, Yin, Jijun, Vinjamuri, Ramana
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 28.10.2022
Schlagworte:	Autoencoder Computational efficiency Computer applications Data science Electroencephalography Feature extraction linear discriminant analysis (LDA) Machine learning PCA Rapid eye movement sleep sleep stages classification
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