Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system

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

doi:10.1109/EMBC.2014.6943846

Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system

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

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

23 Citations (Scopus)

Abstract

A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as the classification method. Using 32-EEG channels, results indicated an improved overall specificity from 76.99% to 82.02%, an improved sensitivity from 74.92 to 78.99% and an improved accuracy from 75.95% to 80.51% when compared to previous studies. The classification using fewer EEG channels, with eleven frontal sites resulted in 77.52% for specificity, 73.78% for sensitivity and 75.65% accuracy being achieved. For ergonomic reasons, the configuration with fewer EEG channels will enhance capacity to monitor fatigue as there is less set-up time required.

Original language	English
Title of host publication	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1338-1341
Number of pages	4
ISBN (Electronic)	9781424479290
ISBN (Print)	9781424479276
DOIs	https://doi.org/10.1109/EMBC.2014.6943846
Publication status	Published - 2 Nov 2014
Externally published	Yes
Event	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: 26 Aug 2014 → 30 Aug 2014

Conference

Conference	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Country/Territory	United States
City	Chicago
Period	26/08/14 → 30/08/14

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10.1109/EMBC.2014.6943846

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Chai, R., Tran, Y., Craig, A., Ling, S. H., & Nguyen, H. T. (2014). Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (pp. 1338-1341). Article 6943846 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EMBC.2014.6943846

Chai, Rifai ; Tran, Yvonne ; Craig, Ashley et al. / Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers (IEEE), 2014. pp. 1338-1341

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title = "Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system",

abstract = "A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as the classification method. Using 32-EEG channels, results indicated an improved overall specificity from 76.99\% to 82.02\%, an improved sensitivity from 74.92 to 78.99\% and an improved accuracy from 75.95\% to 80.51\% when compared to previous studies. The classification using fewer EEG channels, with eleven frontal sites resulted in 77.52\% for specificity, 73.78\% for sensitivity and 75.65\% accuracy being achieved. For ergonomic reasons, the configuration with fewer EEG channels will enhance capacity to monitor fatigue as there is less set-up time required.",

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

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doi = "10.1109/EMBC.2014.6943846",

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Chai, R, Tran, Y, Craig, A, Ling, SH & Nguyen, HT 2014, Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014., 6943846, Institute of Electrical and Electronics Engineers (IEEE), pp. 1338-1341, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, United States, 26/08/14. https://doi.org/10.1109/EMBC.2014.6943846

Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system. / Chai, Rifai; Tran, Yvonne; Craig, Ashley et al.
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers (IEEE), 2014. p. 1338-1341 6943846.

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

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PY - 2014/11/2

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Chai R, Tran Y, Craig A, Ling SH, Nguyen HT. Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers (IEEE). 2014. p. 1338-1341. 6943846 doi: 10.1109/EMBC.2014.6943846

Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system

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