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 proceedingConference proceeding contribution

9 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 languageEnglish
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4460-4463
Number of pages4
ISBN (Print)9781424441235, 9781424441242
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: 31 Aug 20104 Sep 2010

Conference

Conference2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
CountryArgentina
CityBuenos Aires
Period31/08/104/09/10

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  • Cite this

    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). [5625964] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IEMBS.2010.5625964