Subject: EEG, Brain-Computer Interfaces, Noise elimination

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Year: 2021

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Type: Proceeding article

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Title: Review of Drowsiness Detection Machine-Learning Methods Applicable for Non-Invasive Brain-Computer Interfaces

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Author: Gushev, Marjan
Author: Ackovska, Nevena
Author: Zdraveski, Vladimir
Author: Stankov, Emil
Author: Jovanov, Mile
Author: Dinev, Martin
Author: Spasov, Dejan
Author: Gui, Xiaoyan
Author: Zhang, Yanlong
Author: Geng, Li
Author: Zhou, Xiaochuan

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Abstract: This review focuses on the analysis of non-invasive BCI methods, and in particular in the state-of-the-art machine learning-based methods for EEG acquisition. EEG as a tool can be used to detect various states concerning human health, but it can also be used to detect the human’s states such as alertness, interest and even drowsiness. In this paper we focus on this important issue and present some of the ML techniques that can be used, as well as the methodology for noise detection and elimination while using EEG.

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Publisher: IEEE

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Relation: 29th Telecommunications Forum (TELFOR)

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Identifier: oai:repository.ukim.mk:20.500.12188/22844
Identifier: http://hdl.handle.net/20.500.12188/22844

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