EEG-based mental fatigue alarm system using weighted-frequency index

Yunyong Punsawad; Sittichai Aempedchr; Yodchanan Wongsawat; Manukid Parnichkun

EEG-based mental fatigue alarm system using weighted-frequency index

Yunyong Punsawad
, Sittichai Aempedchr
, Yodchanan Wongsawat
, Manukid Parnichkun

Department of Biomedical Engineering

Mahidol University
Asian Institute of Technology Thailand

Research output: Contribution to conference › Paper › peer-review

2 Citations (Scopus)

Abstract

Mental fatigue is another major cause of the serious car accidents. Ability to early predict the mental fatigue phenomenon is hence one of the challenging problems in brain-computer interface (BCI). In this paper, we propose the practical EEG-based mental fatigue alarm system including the weighted-frequency index of the linear combination among EEG theta, alpha and beta rhythms. The proposed system is tested with the simulated driving situations. By using only 1-channel EEG at the temporal area of the brain, more than 90% of prediction accuracies are reported compared to the opinion scores of the users.

Original language	English
Pages	193-196
Number of pages	4
Publication status	Published - 2010
Event	2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010 - Biopolis, Singapore Duration: 14 Dec 2010 → 17 Dec 2010

Conference

Conference	2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010
Country/Territory	Singapore
City	Biopolis
Period	14/12/10 → 17/12/10

UN SDGs

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

SDG 3 Good Health and Well-being

Cite this

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title = "EEG-based mental fatigue alarm system using weighted-frequency index",

abstract = "Mental fatigue is another major cause of the serious car accidents. Ability to early predict the mental fatigue phenomenon is hence one of the challenging problems in brain-computer interface (BCI). In this paper, we propose the practical EEG-based mental fatigue alarm system including the weighted-frequency index of the linear combination among EEG theta, alpha and beta rhythms. The proposed system is tested with the simulated driving situations. By using only 1-channel EEG at the temporal area of the brain, more than 90\% of prediction accuracies are reported compared to the opinion scores of the users.",

author = "Yunyong Punsawad and Sittichai Aempedchr and Yodchanan Wongsawat and Manukid Parnichkun",

year = "2010",

language = "English",

pages = "193--196",

note = "2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010 ; Conference date: 14-12-2010 Through 17-12-2010",

}

EEG-based mental fatigue alarm system using weighted-frequency index. / Punsawad, Yunyong; Aempedchr, Sittichai; Wongsawat, Yodchanan et al.
2010. 193-196 Paper presented at 2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010, Biopolis, Singapore.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - EEG-based mental fatigue alarm system using weighted-frequency index

AU - Punsawad, Yunyong

AU - Aempedchr, Sittichai

AU - Wongsawat, Yodchanan

AU - Parnichkun, Manukid

PY - 2010

Y1 - 2010

N2 - Mental fatigue is another major cause of the serious car accidents. Ability to early predict the mental fatigue phenomenon is hence one of the challenging problems in brain-computer interface (BCI). In this paper, we propose the practical EEG-based mental fatigue alarm system including the weighted-frequency index of the linear combination among EEG theta, alpha and beta rhythms. The proposed system is tested with the simulated driving situations. By using only 1-channel EEG at the temporal area of the brain, more than 90% of prediction accuracies are reported compared to the opinion scores of the users.

AB - Mental fatigue is another major cause of the serious car accidents. Ability to early predict the mental fatigue phenomenon is hence one of the challenging problems in brain-computer interface (BCI). In this paper, we propose the practical EEG-based mental fatigue alarm system including the weighted-frequency index of the linear combination among EEG theta, alpha and beta rhythms. The proposed system is tested with the simulated driving situations. By using only 1-channel EEG at the temporal area of the brain, more than 90% of prediction accuracies are reported compared to the opinion scores of the users.

UR - https://www.scopus.com/pages/publications/79958169810

M3 - Paper

AN - SCOPUS:79958169810

SP - 193

EP - 196

T2 - 2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010

Y2 - 14 December 2010 through 17 December 2010

ER -

EEG-based mental fatigue alarm system using weighted-frequency index

Abstract

Conference

UN SDGs

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