Improving classification rates for use in fatigue countermeasure devices using brain activity

Yvonne Tran; Ashley Craig; Nirupama Wijesuriya; Hung Nguyen

doi:10.1109/IEMBS.2010.5625964

Improving classification rates for use in fatigue countermeasure devices using brain activity

Yvonne Tran, Ashley Craig, Nirupama Wijesuriya, Hung Nguyen^*

^*Corresponding author for this work

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

10 Citations (Scopus)

Abstract

Fatigue can be defined as a state that involves psychological and physical tiredness with a range of symptoms such as tired eyes, yawning and increased blink rate. It has major implications for work place and road safety as well as a negative symptom of many acute and chronic illnesses. As such there has been considerable research dedicated to systems or algorithms that can be used to detect and monitor the onset of fatigue. This paper examines using electroencephalography (EEG) signals to classify fatigue and alert states as a function of subjective self-report, driving performance and physiological symptoms. The results show that EEG classification network for fatigue improved from 75% to 80% when these factors are applied, especially when the data is grouped by subjective selfreport of fatigue with classification accuracy improving to 84.5%.

Original language	English
Title of host publication	2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	4460-4463
Number of pages	4
ISBN (Print)	9781424441235, 9781424441242
DOIs	https://doi.org/10.1109/IEMBS.2010.5625964
Publication status	Published - 2010
Externally published	Yes
Event	2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina Duration: 31 Aug 2010 → 4 Sept 2010

Conference

Conference	2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Country/Territory	Argentina
City	Buenos Aires
Period	31/08/10 → 4/09/10

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10.1109/IEMBS.2010.5625964

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Tran, Y., Craig, A., Wijesuriya, N., & Nguyen, H. (2010). Improving classification rates for use in fatigue countermeasure devices using brain activity. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 (pp. 4460-4463). Article 5625964 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IEMBS.2010.5625964

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title = "Improving classification rates for use in fatigue countermeasure devices using brain activity",

abstract = "Fatigue can be defined as a state that involves psychological and physical tiredness with a range of symptoms such as tired eyes, yawning and increased blink rate. It has major implications for work place and road safety as well as a negative symptom of many acute and chronic illnesses. As such there has been considerable research dedicated to systems or algorithms that can be used to detect and monitor the onset of fatigue. This paper examines using electroencephalography (EEG) signals to classify fatigue and alert states as a function of subjective self-report, driving performance and physiological symptoms. The results show that EEG classification network for fatigue improved from 75% to 80% when these factors are applied, especially when the data is grouped by subjective selfreport of fatigue with classification accuracy improving to 84.5%.",

author = "Yvonne Tran and Ashley Craig and Nirupama Wijesuriya and Hung Nguyen",

year = "2010",

doi = "10.1109/IEMBS.2010.5625964",

language = "English",

isbn = "9781424441235",

pages = "4460--4463",

booktitle = "2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 ; Conference date: 31-08-2010 Through 04-09-2010",

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Tran, Y, Craig, A, Wijesuriya, N & Nguyen, H 2010, Improving classification rates for use in fatigue countermeasure devices using brain activity. in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10., 5625964, Institute of Electrical and Electronics Engineers (IEEE), pp. 4460-4463, 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, Buenos Aires, Argentina, 31/08/10. https://doi.org/10.1109/IEMBS.2010.5625964

Improving classification rates for use in fatigue countermeasure devices using brain activity. / Tran, Yvonne; Craig, Ashley; Wijesuriya, Nirupama et al.
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10. Institute of Electrical and Electronics Engineers (IEEE), 2010. p. 4460-4463 5625964.

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

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T1 - Improving classification rates for use in fatigue countermeasure devices using brain activity

AU - Tran, Yvonne

AU - Craig, Ashley

AU - Wijesuriya, Nirupama

AU - Nguyen, Hung

PY - 2010

Y1 - 2010

N2 - Fatigue can be defined as a state that involves psychological and physical tiredness with a range of symptoms such as tired eyes, yawning and increased blink rate. It has major implications for work place and road safety as well as a negative symptom of many acute and chronic illnesses. As such there has been considerable research dedicated to systems or algorithms that can be used to detect and monitor the onset of fatigue. This paper examines using electroencephalography (EEG) signals to classify fatigue and alert states as a function of subjective self-report, driving performance and physiological symptoms. The results show that EEG classification network for fatigue improved from 75% to 80% when these factors are applied, especially when the data is grouped by subjective selfreport of fatigue with classification accuracy improving to 84.5%.

AB - Fatigue can be defined as a state that involves psychological and physical tiredness with a range of symptoms such as tired eyes, yawning and increased blink rate. It has major implications for work place and road safety as well as a negative symptom of many acute and chronic illnesses. As such there has been considerable research dedicated to systems or algorithms that can be used to detect and monitor the onset of fatigue. This paper examines using electroencephalography (EEG) signals to classify fatigue and alert states as a function of subjective self-report, driving performance and physiological symptoms. The results show that EEG classification network for fatigue improved from 75% to 80% when these factors are applied, especially when the data is grouped by subjective selfreport of fatigue with classification accuracy improving to 84.5%.

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BT - 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

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ER -

Improving classification rates for use in fatigue countermeasure devices using brain activity

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