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

Marjan Gusev; Nevena Ackovska; Vladimir Zdraveski; Emil Stankov; Mile Jovanov; Martin Dinev; Dejan Spasov; Xiaoyan Gui; Yanlong Zhang; Li Geng; Xiaochuan Zhou

doi:10.1109/TELFOR52709.2021.9653239

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

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

Faculty of Electronic and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This review focuses on the analysis of non-invasive Brain-Computer Interface methods, and in particular in the state-of-the-art machine learning-based methods for Electroencephalography (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.

Original language	English
Title of host publication	2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665425841
DOIs	https://doi.org/10.1109/TELFOR52709.2021.9653239
State	Published - 2021
Event	29th Telecommunications Forum, TELFOR 2021 - Virtual, Belgrade, Serbia Duration: 23 Nov 2021 → 24 Nov 2021

Publication series

Name	2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings

Conference

Conference	29th Telecommunications Forum, TELFOR 2021
Country/Territory	Serbia
City	Virtual, Belgrade
Period	23/11/21 → 24/11/21

Keywords

Brain-Computer Interfaces
EEG
Noise elimination

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/TELFOR52709.2021.9653239

Cite this

Gusev, M., Ackovska, N., Zdraveski, V., Stankov, E., Jovanov, M., Dinev, M., Spasov, D., Gui, X., Zhang, Y., Geng, L., & Zhou, X. (2021). Review of Drowsiness Detection Machine-Learning Methods Applicable for Non-Invasive Brain-Computer Interfaces. In 2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings (2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TELFOR52709.2021.9653239

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title = "Review of Drowsiness Detection Machine-Learning Methods Applicable for Non-Invasive Brain-Computer Interfaces",

abstract = "This review focuses on the analysis of non-invasive Brain-Computer Interface methods, and in particular in the state-of-the-art machine learning-based methods for Electroencephalography (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.",

keywords = "Brain-Computer Interfaces, EEG, Noise elimination",

author = "Marjan Gusev and Nevena Ackovska and Vladimir Zdraveski and Emil Stankov and Mile Jovanov and Martin Dinev and Dejan Spasov and Xiaoyan Gui and Yanlong Zhang and Li Geng and Xiaochuan Zhou",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 29th Telecommunications Forum, TELFOR 2021 ; Conference date: 23-11-2021 Through 24-11-2021",

year = "2021",

doi = "10.1109/TELFOR52709.2021.9653239",

language = "英语",

series = "2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings",

}

Gusev, M, Ackovska, N, Zdraveski, V, Stankov, E, Jovanov, M, Dinev, M, Spasov, D, Gui, X, Zhang, Y, Geng, L & Zhou, X 2021, Review of Drowsiness Detection Machine-Learning Methods Applicable for Non-Invasive Brain-Computer Interfaces. in 2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings. 2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 29th Telecommunications Forum, TELFOR 2021, Virtual, Belgrade, Serbia, 23/11/21. https://doi.org/10.1109/TELFOR52709.2021.9653239

Review of Drowsiness Detection Machine-Learning Methods Applicable for Non-Invasive Brain-Computer Interfaces. / Gusev, Marjan; Ackovska, Nevena; Zdraveski, Vladimir et al.
2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Review of Drowsiness Detection Machine-Learning Methods Applicable for Non-Invasive Brain-Computer Interfaces

AU - Gusev, Marjan

AU - Ackovska, Nevena

AU - Zdraveski, Vladimir

AU - Stankov, Emil

AU - Jovanov, Mile

AU - Dinev, Martin

AU - Spasov, Dejan

AU - Gui, Xiaoyan

AU - Zhang, Yanlong

AU - Geng, Li

AU - Zhou, Xiaochuan

PY - 2021

Y1 - 2021

N2 - This review focuses on the analysis of non-invasive Brain-Computer Interface methods, and in particular in the state-of-the-art machine learning-based methods for Electroencephalography (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.

AB - This review focuses on the analysis of non-invasive Brain-Computer Interface methods, and in particular in the state-of-the-art machine learning-based methods for Electroencephalography (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.

KW - Brain-Computer Interfaces

KW - EEG

KW - Noise elimination

UR - https://www.scopus.com/pages/publications/85124610230

U2 - 10.1109/TELFOR52709.2021.9653239

DO - 10.1109/TELFOR52709.2021.9653239

M3 - 会议稿件

AN - SCOPUS:85124610230

T3 - 2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings

BT - 2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 29th Telecommunications Forum, TELFOR 2021

Y2 - 23 November 2021 through 24 November 2021

ER -

Gusev M, Ackovska N, Zdraveski V, Stankov E, Jovanov M, Dinev M et al. Review of Drowsiness Detection Machine-Learning Methods Applicable for Non-Invasive Brain-Computer Interfaces. In 2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. (2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings). doi: 10.1109/TELFOR52709.2021.9653239

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

Abstract

Publication series

Conference

Keywords

UN SDGs

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