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Characterization of Independant Task Neural Correlates of Different Levels of Mental Workload (CARACOg)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT02843919
Recruitment Status : Terminated (not enough subjects)
First Posted : July 26, 2016
Last Update Posted : October 12, 2018
Sponsor:
Information provided by (Responsible Party):
University Hospital, Grenoble

Brief Summary:

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.


Condition or disease Intervention/treatment Phase
Healthy Volunteer Other: Electroencephalography and Electrocardiography Not Applicable

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Study Type : Interventional  (Clinical Trial)
Actual Enrollment : 19 participants
Allocation: N/A
Intervention Model: Single Group Assignment
Masking: None (Open Label)
Primary Purpose: Basic Science
Official Title: Characterization of Independant Task Neural Correlates of Different Levels of Mental Workload
Actual Study Start Date : December 2014
Actual Primary Completion Date : December 2017
Actual Study Completion Date : December 2017

Arm Intervention/treatment
Healthy volunteers
Adults healthy volunteers
Other: Electroencephalography and Electrocardiography



Primary Outcome Measures :
  1. 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


  2. 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


  3. 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




Information from the National Library of Medicine

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Ages Eligible for Study:   20 Years to 40 Years   (Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Criteria

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT02843919


Locations
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France
UniversityHospitalGrenoble
La Tronche, France, 38700
Sponsors and Collaborators
University Hospital, Grenoble
Investigators
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Principal Investigator: Laurent Verceuil, Doctor Grenoble Hospital University
Publications:
Antonenko, P., Paas, F., Grabner, R., & Gog, T. (2010). Using Electroencephalography to Measure Cognitive Load. Educational Psychology Review, 22, 425-438.
Barachant, A. (2012) Commande robuste d'un effecteur par une interface cerveau-machine EEG asynchrone. (Unpublished doctoral dissertation). Université de Grenoble, Grenoble, France.
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.
Cain, B. (2007) "A review of the mental workload literature 1.0".
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.
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.
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.
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.
D. Levendowsk, Z. Konstantinovic, R. Olmstead, and C. Berka (2000). Method for the quantification of human alertness, patent.
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.
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.
Schultheis, H. & Jameson, A. (2004) Assessing Cognitive Load in Adaptive Hypermedia Systems: Physiological and Behavioral Methods. Lecture Notes in Computer Science, 313, 225-234.
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.
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.

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Responsible Party: University Hospital, Grenoble
ClinicalTrials.gov Identifier: NCT02843919    
Other Study ID Numbers: 38RC14.009
First Posted: July 26, 2016    Key Record Dates
Last Update Posted: October 12, 2018
Last Verified: October 2018
Keywords provided by University Hospital, Grenoble:
Electrocardiography
Electroencephalography
Neural Correlates
Mental Workload