- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT02843919
Characterization of Independant Task Neural Correlates of Different Levels of Mental Workload (CARACOg)
The goal is to identify neuro-physiological signatures at several levels of mental workload during the realisation of tasks, performed by all the subjects.
In parallel, there will be a methodological work consisting to develop the classification algorithms, predictives of these levels of mental workload in real time, in purpose to implement a passive brain-machine interface in the best interest of operators that accomplish complex tasks.
Mesures of electro-physiological activity will be recorded in order to approve states of charge in addition to behavioral performances.
Study Overview
Status
Conditions
Intervention / Treatment
Study Type
Enrollment (Actual)
Phase
- Not Applicable
Contacts and Locations
Study Locations
-
-
-
La Tronche, France, 38700
- UniversityHospitalGrenoble
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- Signed informed consent
- Medical examination made before search involvement
- Between 20 and 40 years
- Right-handed
- Minimum study level : Baccalauréat
- Membership of the French social security
- Normal vision and hearing (or corrected to normal)
Exclusion Criteria:
- Sujects included in a clinical or therapeutic trial in progress
- Vision or hearing essential disorder
- Neurological or neuropsychiatric pathology current or gone
- Drug treatment which could alter brain activity (antidepressants, benzodiazepine, lithium etc)
- Pregnant, parturient or breast feeding women
- All other category of protected people
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Basic Science
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
---|---|
Other: Healthy volunteers
Adults healthy volunteers
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Electroencephalography (EEG)
Time Frame: 10 minutes
|
With a EEG helmet. Classical Stemberg's task Stemberg's task with time pressure N-back task Mental arithmetic task 13 minutes MATB Multi-Attribute Task Battery : Divided attention task |
10 minutes
|
Electrooculography (EOG)
Time Frame: 10 minutes
|
Simultaneously to EEG : electrooculography (EOG) will be recorded With a EEG helmet. Classical Stemberg's task Stemberg's task with time pressure N-back task Mental arithmetic task 13 minutes MATB Multi-Attribute Task Battery : Divided attention task |
10 minutes
|
Subjective and behavioral data
Time Frame: 10 minutes
|
KSS scale to evaluate the patient's state of alertness Classical Stemberg's task Stemberg's task with time pressure N-back task Mental arithmetic task 13 minutes MATB Multi-Attribute Task Battery : Divided attention task |
10 minutes
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Laurent Verceuil, Doctor, Grenoble Hospital University
Publications and helpful links
General Publications
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- Gevins A, Smith ME. Neurophysiological measures of working memory and individual differences in cognitive ability and cognitive style. Cereb Cortex. 2000 Sep;10(9):829-39. doi: 10.1093/cercor/10.9.829.
- KIRCHNER WK. Age differences in short-term retention of rapidly changing information. J Exp Psychol. 1958 Apr;55(4):352-8. doi: 10.1037/h0043688. No abstract available.
- Allison BZ, Polich J. Workload assessment of computer gaming using a single-stimulus event-related potential paradigm. Biol Psychol. 2008 Mar;77(3):277-83. doi: 10.1016/j.biopsycho.2007.10.014. Epub 2007 Nov 4.
- Antonenko, P., Paas, F., Grabner, R., & Gog, T. (2010). Using Electroencephalography to Measure Cognitive Load. Educational Psychology Review, 22, 425-438.
- Baldwin CL, Penaranda BN. Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification. Neuroimage. 2012 Jan 2;59(1):48-56. doi: 10.1016/j.neuroimage.2011.07.047. Epub 2011 Jul 30.
- Barachant, A. (2012) Commande robuste d'un effecteur par une interface cerveau-machine EEG asynchrone. (Unpublished doctoral dissertation). Université de Grenoble, Grenoble, France.
- Bashashati A, Fatourechi M, Ward RK, Birch GE. A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J Neural Eng. 2007 Jun;4(2):R32-57. doi: 10.1088/1741-2560/4/2/R03. Epub 2007 Mar 27.
- Berka C, Levendowski DJ, Lumicao MN, Yau A, Davis G, Zivkovic VT, Olmstead RE, Tremoulet PD, Craven PL. EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat Space Environ Med. 2007 May;78(5 Suppl):B231-44.
- Besserve, M., Martinerie, J., & Garnero, L. (2008). Non-invasive classification of cortical activities for brain computer interface: A variable selection approach (p. 1063-1066). IEEE.
- Brouwer AM, Hogervorst MA, van Erp JB, Heffelaar T, Zimmerman PH, Oostenveld R. Estimating workload using EEG spectral power and ERPs in the n-back task. J Neural Eng. 2012 Aug;9(4):045008. doi: 10.1088/1741-2560/9/4/045008. Epub 2012 Jul 25.
- Cain, B. (2007) "A review of the mental workload literature 1.0".
- Christensen JC, Estepp JR, Wilson GF, Russell CA. The effects of day-to-day variability of physiological data on operator functional state classification. Neuroimage. 2012 Jan 2;59(1):57-63. doi: 10.1016/j.neuroimage.2011.07.091. Epub 2011 Aug 5.
- Comstock, J. R., Jr., & Arnegard, R. J. (1992) The Multi-Attribute Task Battery for human operator workload and strategic behavior research (NASA TM-104174). Hampton, Virginia: NASA Langley Research Center.
- Dussault C, Jouanin JC, Philippe M, Guezennec CY. EEG and ECG changes during simulator operation reflect mental workload and vigilance. Aviat Space Environ Med. 2005 Apr;76(4):344-51.
- Fu, S. & Parasuraman, R. (2007) Event-related potentials (ERPs) in Neuroergonomics. . In Parasuraman, R. & Rizzo, M. (Eds), Neuroergonomics: The brain at work (pp. 15-31). New York, NY: Oxford University Press, Inc.
- George, L., & Lécuyer, A. (2010). An overview of research on " passive " brain-computer interfaces for implicit human-computer interaction. International Conference on Applied Bionics and Biomechanics (ICABB), Venice, Italy, October 14-16, 2010.
- Gevins, A., & Smith, M. E. (2003). Neurophysiological measures of cognitive workload during human-computer interaction. Theoretical Issues in Ergonomics Science, 1, 113-131.
- Gevins, A. & Smith, M. E. (2007) Electroencephalography (EEG) in Neuroergnomics. In Parasuraman, R. & Rizzo, M. (Eds), Neuroergonomics: The brain at work (pp. 15-31). New York, NY: Oxford University Press, Inc.
- Gomarus HK, Althaus M, Wijers AA, Minderaa RB. The effects of memory load and stimulus relevance on the EEG during a visual selective memory search task: an ERP and ERD/ERS study. Clin Neurophysiol. 2006 Apr;117(4):871-84. doi: 10.1016/j.clinph.2005.12.008. Epub 2006 Jan 25.
- Graimann, B, Allison, B. & Pfurstscheller, G. (2010) Brain-computer interfaces: A gentle introduction. In Graimann, B, Allison, B. & Pfurstscheller, G. (Eds) Brain-computer interfaces: Revolutionizing human-computer interaction, (pp. 1-28), Berlin Heidelberg, Springer-Verlag.
- Grimes, D., Tan, D. S., Hudson, S. E., Shenoy, P., & Rao, R. P. N. (2008). Feasibility and pragmatics of classifying working memory load with an electroencephalograph (p. 835). ACM Press.
- Heger, D., Putze, F., & Schultz, T. (2010). Online workload recognition from EEG data during cognitive tests and human-machine interaction. KI 2010: Advances in Artificial Intelligence, 410-417.
- Henelius A, Hirvonen K, Holm A, Korpela J, Muller K. Mental workload classification using heart rate metrics. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:1836-9. doi: 10.1109/IEMBS.2009.5332602.
- Holland MK, Tarlow G. Blinking and mental load. Psychol Rep. 1972 Aug;31(1):119-27. doi: 10.2466/pr0.1972.31.1.119. No abstract available.
- Holm A, Lukander K, Korpela J, Sallinen M, Muller KM. Estimating brain load from the EEG. ScientificWorldJournal. 2009 Jul 14;9:639-51. doi: 10.1100/tsw.2009.83.
- Honal, M., & Schultz, T. (2008). Determine task demand from brain activity. In Proceedings of the 3rd International Conference on Bio-inspired Systems and Signal Processing.
- Kahol K, Smith M, Brandenberger J, Ashby A, Ferrara JJ. Impact of fatigue on neurophysiologic measures of surgical residents. J Am Coll Surg. 2011 Jul;213(1):29-34; discussion 34-6. doi: 10.1016/j.jamcollsurg.2011.03.028. Epub 2011 Apr 23.
- Kok A. On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology. 2001 May;38(3):557-77. doi: 10.1017/s0048577201990559.
- Koles ZJ, Flor-Henry P. Mental activity and the e.e.g.: task and workload related effects. Med Biol Eng Comput. 1981 Mar;19(2):185-94. doi: 10.1007/BF02442714. No abstract available.
- D. Levendowsk, Z. Konstantinovic, R. Olmstead, and C. Berka (2000). Method for the quantification of human alertness, patent.
- Lotte F, Congedo M, Lecuyer A, Lamarche F, Arnaldi B. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng. 2007 Jun;4(2):R1-R13. doi: 10.1088/1741-2560/4/2/R01. Epub 2007 Jan 31.
- McDonald NJ, Soussou W. QUASAR's QStates cognitive gauge performance in the cognitive state assessment competition 2011. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:6542-6. doi: 10.1109/IEMBS.2011.6091614.
- Miller MW, Rietschel JC, McDonald CG, Hatfield BD. A novel approach to the physiological measurement of mental workload. Int J Psychophysiol. 2011 Apr;80(1):75-8. doi: 10.1016/j.ijpsycho.2011.02.003. Epub 2011 Feb 20.
- Missonnier P, Deiber MP, Gold G, Millet P, Gex-Fabry Pun M, Fazio-Costa L, Giannakopoulos P, Ibanez V. Frontal theta event-related synchronization: comparison of directed attention and working memory load effects. J Neural Transm (Vienna). 2006 Oct;113(10):1477-86. doi: 10.1007/s00702-005-0443-9. Epub 2006 Apr 11.
- Natani, K., & Gomer, F. E. (1981). Electrocortical activity and operator workload: A comparison of changes in the electroencephalogram and in event-related potentials. (McDonnell Douglas Technical Report E2427). Long Beach, CA: McDonnell Douglas Corporation.
- Nourbakhsh, N., Wang, Y., & Chen, F. (2013). GSR and Blink Features for Cognitive Load Classification. In P. Kotzé, G. Marsden, G. Lindgaard, J. Wesson, & M. Winckler (Éd.), Human-Computer Interaction - INTERACT 2013 (Vol. 8117, p. 159-166). Berlin, Heidelberg: Springer Berlin Heidelberg.
- Ossandon T, Jerbi K, Vidal JR, Bayle DJ, Henaff MA, Jung J, Minotti L, Bertrand O, Kahane P, Lachaux JP. Transient suppression of broadband gamma power in the default-mode network is correlated with task complexity and subject performance. J Neurosci. 2011 Oct 12;31(41):14521-30. doi: 10.1523/JNEUROSCI.2483-11.2011.
- Putze, F., Jarvis, J. P., & Schultz, T. (2010) Multimodal Recognition of Cognitive Workload for Multitasking in the Car. International Conference on Pattern Recognition (ICPR), 20, 3748-3751.
- Schober F, Schellenberg R, Dimpfel W. Reflection of mental exercise in the dynamic quantitative topographical EEG. Neuropsychobiology. 1995;31(2):98-112. doi: 10.1159/000119179.
- Schultheis, H. & Jameson, A. (2004) Assessing Cognitive Load in Adaptive Hypermedia Systems: Physiological and Behavioral Methods. Lecture Notes in Computer Science, 313, 225-234.
- Sternberg S. High-speed scanning in human memory. Science. 1966 Aug 5;153(3736):652-4. doi: 10.1126/science.153.3736.652.
- Sternberg S. Memory-scanning: mental processes revealed by reaction-time experiments. Am Sci. 1969 Winter;57(4):421-57. No abstract available.
- Tanaka Y, Yamaoka K. Blink activity and task difficulty. Percept Mot Skills. 1993 Aug;77(1):55-66. doi: 10.2466/pms.1993.77.1.55.
- Tremoulet, P. D., Craven, P. L., Regli, S. H., Wilcox, S., Barton, J., Stibler and K., Clark, M. (2009). Workload-Based Assessment of a User Interface Design. In V. G. Duffy (Éd.), Digital Human Modeling (Vol. 5620, p. 333-342). Berlin, Heidelberg: Springer Berlin Heidelberg.
- Veltman JA, Gaillard AW. Physiological indices of workload in a simulated flight task. Biol Psychol. 1996 Feb 5;42(3):323-42. doi: 10.1016/0301-0511(95)05165-1.
- Wang Z, Hope RM, Wang Z, Ji Q, Gray WD. Cross-subject workload classification with a hierarchical Bayes model. Neuroimage. 2012 Jan 2;59(1):64-9. doi: 10.1016/j.neuroimage.2011.07.094. Epub 2011 Aug 16.
- Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002 Jun;113(6):767-91. doi: 10.1016/s1388-2457(02)00057-3.
- Zander, T.O., Kothe, C., Jatzev, S. & Gaertner, M. (2010) Enhancing human-computer interaction with input from active and passive brain-computer interfaces. In Tan, D.S. & Nijholt, A. (Eds) Brain-computer interfaces: Applying our minds to human-computer interaction (pp. 181-196), London, Springer-Verlag.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimate)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- 38RC14.009
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