Advertisement

QEEG Biomarkers: Assessment and Selection of Special Operators, and Improving Individual Performance

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8027)

Abstract

Future military special operator selection and education programs will take advantage of state-of-the-art neuroimaging and normative statistical tools in the creation of a customized database of EEG patterns gathered from top performing specialists over their careers. Such a quantitative EEG Normative Database (qEND) will function as the benchmark for screening, assessment, selection and even training of targeted individuals required to work effectively as operators under extreme stresses and for extended periods. This assumption implies that an improved warfighter selection and training pedagogy will embrace the concept of a “model” brain activity pattern (BAP) that represents a warfighter at peak potential and in a highly focused and resilient state of mind. It also implies that this model BAP can be used to: 1) identify biomarkers of positive traits in candidates for specialized training programs, and 2) reduce stress and improve sleep and training performance of program selectees using guided EEG neurofeedback to maintain an optimal BAP. One such statistical qEND (NeuroGuide) is used clinically in the assessment and diagnosis of EEG imbalances specifically related to neurological and behavioral disorders, as well as for guiding individual brain pattern changes through the use of neurofeedback training (NT).

To evaluate qEEG for monitoring an individual’s BAP changes and potentially improving mood and work performance, two military specialists with leadership experience underwent a program of pre- and post-EEG recordings and 20 neurofeedback training (NT) sessions. Here, the NeuroGuide database was used to determine how each participant’s BAP differed from the age-matched group norms, and it was also used during the NT process to inform the software of the differences from the norms at each of the 4 training sites used to adjust the trainees EEG towards the direction of “normal”.

Changes from the NT program were assessed pre- and post-intervention using seven neuropsychological assessments of mood, anxiety, sleep, work performance and life satisfaction. In addition, one subject had a series of blood draws taken over the course of the NT program to evaluate changes in his plasma Cortisol; a reliable biomarker of stress level. Both subjects reported reduced levels of anxiety, impulsivity and anger, and improved mood and life satisfaction after the 20-session NT intervention.

Keywords

Assessment and Selection Biomarkers Quantitative EEG Neurofeedback Normative Statistics Training Technologies Training Policy 

References

  1. 1.
    Pascual-Leone, A., Freitas, C., Oberman, L., Horvath, J.C., et al.: Characterizing Brain Cortical Plasticity and Network Dynamics Across the Age-Span in Health and Disease with TMS-EEG and TMS-fMRI. Brain Topogr. 24(3-4), 302–315 (2011)CrossRefGoogle Scholar
  2. 2.
    Chu, C.J., Kramer, M.A., Pathmanathan, J., Bianchi, M.T., Westover, M.B., Wizon, L., Cash, S.S.: Emergence of stable functional networks in long-term human electroencephalography. J. Neurosci. 32(8), 2703–2713 (2012)CrossRefGoogle Scholar
  3. 3.
    Thatcher, R.W., Walker, R.A., Biver, C.J., North, D.M., Curtin, R.: Sensitivity and Specificity of an EEG Normative Database: Validation and Clinical Correlation. J. Neurotherapy 7(3/4), 87–121 (2003)CrossRefGoogle Scholar
  4. 4.
    Collura, T.F.: Neuronal Dynamics in Relation to Normative Electroencephalography Assessment and Training. Biofeedback 36(4), 134–139 (2009)Google Scholar
  5. 5.
    Hoedlmoser, K., Pecherstorfer, T., Gruber, G., Anderer, P., Doppelmayr, M., Klimesch, W., Schabus, M.: Instrumental conditioning of human sensorimotor rhythm (12-15 Hz) and its impact on sleep as well as declarative learning. Sleep 31(10), 1401–1408 (2008)Google Scholar
  6. 6.
    Michael, A.J., Krishnaswamy, S., Mohamed, J.: An open label study of the use of EEG biofeedback using beta training to reduce anxiety for patients with cardiac events. Neuropsychiatr. Dis. Treat. 1(4), 357–363 (2005)Google Scholar
  7. 7.
    Giordano, J., DuRousseau, D.R.: Toward Right and Good Use of Brain-Machine Interfacing Neurotechnologies: Ethical Issues, and Implications for Guidelines and Policy. Cog. Technol. 15(2), 5–10 (2011)Google Scholar
  8. 8.
    Quan, M., Zheng, C., Zhang, N., Han, D., Tian, Y., Zhang, T., Yang, Z.: Impairments of behavior, information flow between thalamus and cortex, and prefrontal cortical synaptic plasticity in an animal model of depression. Brain Res. Bull. 85(3-4), 109–116 (2011)CrossRefGoogle Scholar
  9. 9.
    Flo, E., Steine, I., Blågstad, T., Grønli, J., Pallesen, S., Portas, C.: Transient changes in frontal alpha asymmetry as a measure of emotional and physical distress during sleep. Brain Res. 1367, 234–249 (2011) (Epub October 1, 2010)Google Scholar
  10. 10.
    Dias-Ferreira, E., Sousa, J.C., Melo, I., Morgado, P., Mesquita, A.R., Cerqueira, J.J., Costa, R.M., Sousa, N.: Chronic stress causes frontostriatal reorganization and affects decision-making. Science 325(5940), 621–625 (2009)CrossRefGoogle Scholar
  11. 11.
    Leuchter, A.F., Cook, I.A., Hunter, A.M., Cai, C., Horvath, S.: Resting-State Quantitative Electroencephalography Reveals Increased Neurophysiologic Connectivity in Depression. PLoS ONE 7(2), e32508 (2012), doi:10.1371/journal.pone.0032508.Google Scholar
  12. 12.
    Sirota, A., Buzsáki, G.: Interaction between neocortical and hippocampal networks via slow oscillations. Thalamus Relat. Syst. 3(4), 245–259 (2005)CrossRefGoogle Scholar
  13. 13.
    Menon, V., Uddin, L.: Saliency, switching, attention and control: a network model of insula function. Brain Struct. Funct. 214(5-6), 655–667 (2010) (Epub 2010 May 29, 2010), doi:10.1007/s00429-010-0262-0.Google Scholar
  14. 14.
    Brembs, B.: Operant conditioning in invertebrates. Curr. Opin. Neurobiol. 13(6), 710–717 (2003)CrossRefGoogle Scholar
  15. 15.
    Gruzelier, J.: A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. Cogn. Process. 10 (suppl. 1), 101–109 (2009) (Epub December 11, 2008 )Google Scholar
  16. 16.
    DuRousseau, D.R., Mindlin, G., Insler, J., Levin II: Operational Study to Evaluate Music-Based Neurotraining at Improving Sleep Quality, Mood and Daytime Function in a First Responder Population. Journal Neurotherapy 4, 389–398 (2011)CrossRefGoogle Scholar
  17. 17.
    Collura, T.F., Thatcher, R.W.: Clinical benefit to patients suffering from recurrent migraine headaches and who opted to stop medication and take a neurofeedback treatment series. Clin. EEG Neurosci. 42(2), VIII–IX (2011)Google Scholar
  18. 18.
    Scharnowski, F., Hutton, C., Josephs, O., Weiskopf, N., Rees, G.: Improving Visual Perception through Neurofeedback. J. of Neuroscience 32(49), 17830–17841 (2012)CrossRefGoogle Scholar
  19. 19.
    Ros, T., Moseley, M.J., Bloom, P.A., Benjamin, L., Parkinson, L.A., Gruzelier, J.H.: Optimizing microsurgical skills with EEG neurofeedback. BMC Neurosci. 10, 87 (2009)CrossRefGoogle Scholar
  20. 20.
    Tacker, M.M., Leach, C.S., Owen, C.A., Rummel, J.: Levels of cortisol, corticosterone, cortisone and 11-deoxycoritsol in the plasma of stressed and unstressed subjects. J. Endocrinol. 76(1), 165–166 (1978)CrossRefGoogle Scholar
  21. 21.
    Swigar, M.E., Kolakowska, T., Quinlan, D.: Plasma cortisol levels in depression and other psychiatric disorders: a study of newly admitted psychiatric patients. Psychol. Med. 9(3), 449–455 (1979)CrossRefGoogle Scholar
  22. 22.
    Plischke, H., DuRousseau, D., Giordano, J.: EEG-based Neurofeedback– The Promise of Neurotechnology and Need for Neuroethically-informed Guidelines and Policies. J. Ethics Biol. Engineer. Med. (July 2012), doi:10.1615/EthicsBiologyEngMed.2012004853Google Scholar
  23. 23.
    Canli, T., Brandon, S., Casebeer, W., Crowley, P.J., DuRousseau, D., Greely, H., Güzeldere, G., Pascual-Leone, A.: Neuroethics and National Security. The American Journal of Bioethics 7(5), 3–13 (2007)CrossRefGoogle Scholar
  24. 24.
    Gianotti, L.R.R., Figner, B., Ebstein, R.P., Knoch, D.: Why some people discount more than others: baseline activation in the dorsal PFC mediates the link between COMT genotype and impatient choice. Frontiers in Neuroscience, Decision Neuroscience 6, Article 54, 1–12 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.PEAK Neurotraining SolutionsSterlingU.S.A.

Personalised recommendations