Healthy VolunteerCARACOgNCT02843919
Characterization of Independant Task Neural Correlates of Different Levels of Mental Workload
A study testing Electroencephalography and Electrocardiography in people with Healthy Volunteer, run by University Hospital, Grenoble at 1 site in France.
Trial 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… Read more about this study
- Who is it for?
- Ages 20 to 40 yearsHealthy volunteers can take part
- Conditions the study is for
- Healthy Volunteer
- What is being tested?
- Electroencephalography and Electrocardiography
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 19 participants
- Where
- 1 siteFrance
- Study dates
- Dec 2014to Dec 2017
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 to 40 years
- All sexes
- Healthy volunteers accepted
- Adult
Who may be able to join
- 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)
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
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.
- Conditions
- Healthy Volunteer
- Being tested
- Electroencephalography and Electrocardiography
- Keywords
- Electrocardiography, Electroencephalography, Neural Correlates, Mental Workload
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Electroencephalography and ElectrocardiographyOther
| Study group | Role | What they receive |
|---|---|---|
| Healthy volunteers | Other | Adults healthy volunteers Receives: Other: Electroencephalography and Electrocardiography |
What is being measured
The primary outcome is the main question the study is designed to answer.
Electroencephalography (EEG)
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
Measured: 10 minutes
Electrooculography (EOG)
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
Measured: 10 minutes
Subjective and behavioral data
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
Measured: 10 minutes
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Jul 2016 (estimated)
- Study startDec 2014
- Primary completionDec 2017
- Study completionDec 2017
Why it stopped: not enough subjects
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (49)Papers the sponsor has linked to this study.
- Other trials for Healthy VolunteerStudies of the same condition, open and under way.
- More trials run by University Hospital, GrenobleThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 19 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Hospital, Grenoble
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Laurent Verceuil, Doctor, Principal investigator, Grenoble Hospital University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02843919
- Sponsor's protocol ID
- 38RC14.009
- Acronym
- CARACOg
- Status last verified
- Oct 2018
- First submitted
- 11 Jul 2016
- Last updated
- 12 Oct 2018
- Results posted
- No
- Data monitoring committee
- No
Publications
Akerstedt T, Gillberg M. Subjective and objective sleepiness in the active individual. Int J Neurosci. 1990 May;52(1-2):29-37. doi: 10.3109/00207459008994241.
PubMed 2265922Allison 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.
PubMed 18093717Antonenko, 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.
PubMed 21835243Barachant, 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.
PubMed 17409474Berka 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.
PubMed 17547324Besserve, 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.
PubMed 22832068Cain, 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.
PubMed 21840403Comstock, 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.
PubMed 15828633Fu, 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.
Study at a glance
From the registry record, last updated by the sponsor on 12 Oct 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Characterization of Independant Task Neural Correlates of Different Levels of Mental Workload
- ClinicalTrials.gov ID
- NCT02843919
- Acronym / sponsor ID
- CARACOg / 38RC14.009
- Conditions
- Healthy Volunteer
- Keywords
- Electrocardiography, Electroencephalography, Neural Correlates, Mental Workload
- Study type
- Interventional
- Recruitment status
- TerminatedReason stopped: not enough subjects
- Enrolment
- 19 participants (actual)
- Lead sponsor
- University Hospital, GrenobleUniversity, hospital or other
- Study start
- Dec 2014 (actual)
- Primary completion
- Dec 2017 (actual)
- Study completion
- Dec 2017 (actual)
- First posted
- 26 Jul 2016 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Healthy volunteers | Other | Other: Electroencephalography and ElectrocardiographyAdults healthy volunteers |
Who may qualify
- Ages
- 20 to 40 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (7 listed)
- Signed informed consent
- Medical examination made before search involvement
- Between 20 and 40 years
- Right-handed
- Minimum study level : Baccalauréat
Key exclusion criteria (6 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Electroencephalography and Electrocardiography | Other | Healthy volunteers |
Primary outcome
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
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
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
Results and safety
The study was stopped early (reason given: not enough subjects). Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Oct 2018.
- Background: PubMed 2265922Akerstedt T, Gillberg M. Subjective and objective sleepiness in the active individual. Int J Neurosci. 1990 May;52(1-2):29-37. doi: 10.3109/00207459008994241.
- Background: PubMed 18093717Allison 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.
- BackgroundAntonenko, P., Paas, F., Grabner, R., & Gog, T. (2010). Using Electroencephalography to Measure Cognitive Load. Educational Psychology Review, 22, 425-438.
- Background: PubMed 21835243Baldwin 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.
- BackgroundBarachant, A. (2012) Commande robuste d'un effecteur par une interface cerveau-machine EEG asynchrone. (Unpublished doctoral dissertation). Université de Grenoble, Grenoble, France.
- Background: PubMed 17409474Bashashati 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.
- Background: PubMed 17547324Berka 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.
- BackgroundBesserve, M., Martinerie, J., & Garnero, L. (2008). Non-invasive classification of cortical activities for brain computer interface: A variable selection approach (p. 1063-1066). IEEE.
- Background: PubMed 22832068Brouwer 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.
- BackgroundCain, B. (2007) "A review of the mental workload literature 1.0".
- Background: PubMed 21840403Christensen 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.
- BackgroundComstock, 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Laurent Verceuil, Doctor principal investigator
Grenoble Hospital University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Electroencephalography and Electrocardiography in people with Healthy Volunteer, run by University Hospital, Grenoble at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: stopped early, as of the sponsor's last update on 12 Oct 2018. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was stopped early (reason given: not enough subjects). Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Electroencephalography (EEG) (time frame: 10 minutes). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT02843919, which also lists 49 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Oct 2018.