Developing neurophysiological metrics for the assessment of mental workload and the functional state of the brain

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Aalto-yliopiston teknillinen korkeakoulu | Doctoral thesis (article-based)
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Date
2010
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Mcode
Degree programme
Language
en
Pages
Verkkokirja (1142 KB, 83 s.)
Series
Department of Biomedical Engineering and Computational Science publications. A, Report, 18
Abstract
Modern working environments are often information intensive and work performance requires acting on multiple tasks simultaneously, i.e., multitasking. Also, irregular and prolonged work schedules, shift work and night work are typical in many work sectors. This causes both acute and chronic sleep loss, which results in performance impairment, such as increased reaction times, memory difficulties, cognitive slowing, and lapses of attention. Long lasting sleep loss and sustained overloading increase the risk of human errors and may cause work related stress and even occupational burn-out. According to the Finnish Occupational Safety and Health Act (738/2002, Työturvallisuuslaki), Section 25 Avoiding and reducing workloads, an employer should assess the workload the employee is exposed to. Despite the fact that this important issue is enacted in the law, the objective measures to assess the workload are lacking. This Thesis reviews neurophysiologic methods for assessment of cognitive workload and sleep loss. Then it describes experimental studies where the feasibility of conventional event related potential (ERP) and electroencephalography (EEG) methods were tested both in assessment of internal state of participants during challenging task performance after sleep debt and in diagnostic of work-related central nervous system disorder. After that, methodological improvements both on ERPs and EEG metrics are shown: ERPs were analysed with a single-trial method, and EEG methodology was developed for estimation of both internal (caused by sleep loss) and external (caused by task demands) load. The methods were tested in healthy controls. The most promising metric to study overall brain load, including both cognitive workload as well as sleep loss, is suggested to be theta Fz / alpha Pz -ratio. It increases both with growing cognitive workload level and time spent awake, being sensitive also to sleep loss. This metric is possible to measure both in laboratory and in the field conditions. Measurements may be carried out even during real work tasks, at least in professions where most work is done in office-like environments. As the ratio increases with cognitive brain load similar to the heartbeat with increasing physical load, the ratio was named "brainbeat".
Description
Supervising professor
Ilmoniemi, Risto, Prof.
Thesis advisor
Muller, Kiti, Dr.
Keywords
brain, electroencephalography, EEG, event related potential, ERP, mental, workload
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Parts
  • [Publication 1]: Petra Keski-Säntti, Anu Holm, Ritva Akila, Katinka Tuisku, Tero Kovala, and Markku Sainio. 2007. P300 of auditory event related potentials in occupational chronic solvent encephalopathy. NeuroToxicology, volume 28, number 6, pages 1230-1236.
  • [Publication 2]: Anu Holm, Perttu O. Ranta-aho, Mikael Sallinen, Pasi A. Karjalainen, and Kiti Müller. 2006. Relationship of P300 single-trial responses with reaction time and preceding stimulus sequence. International Journal of Psychophysiology, volume 61, number 2, pages 244-252.
  • [Publication 3]: P. Keski-Säntti, T. Kovala, A. Holm, H. K. Hyvärinen, and M. Sainio. 2008. Quantitative EEG in occupational chronic solvent encephalopathy. Human & Experimental Toxicology, volume 27, number 4, pages 315-320.
  • [Publication 4]: Mikael Sallinen, Anu Holm, Jaana Hiltunen, Kati Hirvonen, Mikko Härmä, Jukka Koskelo, Mika Letonsaari, Ritva Luukkonen, Jussi Virkkala, and Kiti Müller. 2008. Recovery of cognitive performance from sleep debt: do a short rest pause and a single recovery night help? Chronobiology International, volume 25, numbers 2-3, pages 279-296.
  • [Publication 5]: Anu Holm, Kristian Lukander, Jussi Korpela, Mikael Sallinen, and Kiti M. I. Müller. 2009. Estimating brain load from the EEG. TheScientificWorldJOURNAL, volume 9, pages 639-651. © 2009 by authors.
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