Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification

Rifai Chai; Ganesh R. Naik; Sai Ho Ling; Yvonne Tran; Ashley Craig; Hung T. Nguyen

doi:10.1109/EMBC.2017.8037196

Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification

Rifai Chai
, Ganesh R. Naik
, Sai Ho Ling
, Yvonne Tran
, Ashley Craig
, Hung T. Nguyen

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

19 Citations (Scopus)

Abstract

This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.

Original language	English
Title of host publication	2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Smarter Technology for a Healthier World, EMBC 2017
Place of Publication	South Korea
Publisher	Institute of Electrical and Electronics Engineers
Pages	1808-1811
Number of pages	4
ISBN (Electronic)	9781509028092
ISBN (Print)	9781509028108
DOIs	https://doi.org/10.1109/EMBC.2017.8037196
Publication status	Published - 13 Sept 2017
Externally published	Yes
Event	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: 11 Jul 2017 → 15 Jul 2017

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	11/07/17 → 15/07/17

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1109/EMBC.2017.8037196Licence: In Copyright

Cite this

Chai, R., Naik, G. R., Ling, S. H., Tran, Y., Craig, A., & Nguyen, H. T. (2017). Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 (pp. 1808-1811). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2017.8037196

Chai, Rifai ; Naik, Ganesh R. ; Ling, Sai Ho et al. / Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 . South Korea : Institute of Electrical and Electronics Engineers, 2017. pp. 1808-1811 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{8cc770c91d794d6e8ce2295caef70dd4,

title = "Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification",

abstract = "This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8\%, specificity of 74.3\% and an accuracy of 75.5\%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.",

author = "Rifai Chai and Naik, \{Ganesh R.\} and Ling, \{Sai Ho\} and Yvonne Tran and Ashley Craig and Nguyen, \{Hung T.\}",

year = "2017",

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doi = "10.1109/EMBC.2017.8037196",

language = "English",

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series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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Chai, R, Naik, GR, Ling, SH, Tran, Y, Craig, A & Nguyen, HT 2017, Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification. in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 . Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers, South Korea, pp. 1808-1811, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, Jeju Island, Korea, Republic of, 11/07/17. https://doi.org/10.1109/EMBC.2017.8037196

Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification. / Chai, Rifai; Naik, Ganesh R.; Ling, Sai Ho et al.
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 . South Korea: Institute of Electrical and Electronics Engineers, 2017. p. 1808-1811 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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

TY - GEN

T1 - Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification

AU - Chai, Rifai

AU - Naik, Ganesh R.

AU - Ling, Sai Ho

AU - Tran, Yvonne

AU - Craig, Ashley

AU - Nguyen, Hung T.

PY - 2017/9/13

Y1 - 2017/9/13

N2 - This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.

AB - This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.

UR - https://www.scopus.com/pages/publications/85032219714

U2 - 10.1109/EMBC.2017.8037196

DO - 10.1109/EMBC.2017.8037196

M3 - Conference contribution

C2 - 29060240

AN - SCOPUS:85032219714

SN - 9781509028108

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 1808

EP - 1811

BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

PB - Institute of Electrical and Electronics Engineers

CY - South Korea

T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017

Y2 - 11 July 2017 through 15 July 2017

ER -

Chai R, Naik GR, Ling SH, Tran Y, Craig A, Nguyen HT. Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 . South Korea: Institute of Electrical and Electronics Engineers. 2017. p. 1808-1811. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2017.8037196

Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification

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