Scientific background

The important part of BrainID® -mapping is BrainMind Audit® profile, which is based on basic neuroscience research conducted since the 1970s (references at the end).

BrainMind Audit® profile

The BrainMind Audit® profile describes nine measurable traits or markers of brain activity that are combined with mental functions based on available research data. The markers to be measured are identifiable, permanent and reliable to measure. Each measurement is compared to standard data according to the principles of neurometry. This provides a profile of the optimality of the cognitive and emotional functioning of an individual’s brain.

These nine markers are based on the scientific expertise of the BM-Science researchers who developed the survey, as evidenced by more than 170 published articles, research data from more than 400 neuroscience publications related to these markers, and a unique innovation developed from this work.
BM-Science – Brain & Mind Technologies Research Centre | www.bm-science.com

BrainID®-service

The scientific invention, combined with mentoring, has been commercialized into the BrainID® service, which aims to benefit people at work in particular by stimulating and supporting their own aspirations for well-being.

The measurement result is functional, not clinical. The BrainID® service excludes all uses where the criteria are not met, as is not the case for medical use.

The research done supports the application of BrainID® mapping as part of mentoring, job coaching and coaching. In addition to commercial use, BrainMind Audit® is also in active research use. Ongoing further research will benefit further development.

BrainID®-mentor

In BrainID® service BrainMind Audit® acts as a mentor tool. Mentoring is a discussion of the research outcome and the ideas it evokes. Supported by discussions with the mentor, the client finds the best changes in their own daily life that improve their well-being. There is currently research in the scientific evaluation process to find out how mentoring helps the client commit to and implement the changes they want, such as lifestyle changes.

BrainID Oy certifies as mentors only trained working life development professionals (coaches, coaches, work supervisors) who have supplemented their skills with additional training related to the use of the method.

qEEG measurement is easy and safe and does not cause any side effects
The information obtained is confidential, only to be disclosed to the person being measured
The data will be processed as required by data protection law

BM-Science: Alexander & Andrew Fingelkurts, Carlos Neves

The scientific article from the BM-Science development team on the effectiveness of BrainID

The new study reporting first ever neuroimaging screening of the coaching training is published in “Coaching: An International Journal of Theory, Research and Practice”…

BrainMind Audit® -profile consists of nine markers. Each dimension of the profile has a strong correlation with neurological markers.

Vigilance, voltage
Performance speed
Internal focusing
Emotional motivation tendency
Sociality
Orientation of anxiety
Stress management
Total brain resources
Deviation from optimal brain function

References

Published research data underlying BrainID® mapping
Fingelkurts A.A., Fingelkurts A.A., Kallio-Tamminen T. EEG-guided meditation: A personalized approach. J. Physiol. Paris 2015; 109: 180–190.

Reliability of qEEG measurement
Begleiter H., Porjesz B. Genetics of human brain oscillations. Int. J. Psychophysiol. 2006; 60: 162–171.
Fingelkurts A.A., Fingelkurts A.A., Ermolaev V.A., Kaplan A.Y. Stability, reliability and consistency of the compositions of brain oscillations. Int. J. Psychophysiol. 2006; 59: 116–126.
Gasser T., Bächer P., Steinberg H. Test–retest reliability of spectral parameters of the EEG. Electroencephalogr. Clin. Neurophysiol. 1985; 60: 312–319.
Huang J., Sander C., Jawinski P., Ulke C., Spada J., Hegerl U., Hensch, T. Test-retest reliability of brain arousal regulation as assessed with VIGALL 2.0., 2015.
Kondacs A., Szabo M. Long-term intra-individual variability of the background EEG in normals. Clin. Neurophysiol. 1999; 110: 1708–1716.
Smit D.J.A., Postuma D., Boomsma D.I., De Geus E.J.C. Heritability of background EEG across the power spectrum. Psychophysiology 2005; 42: 691–697.
Smit C.M., Wright M.J., Hansell N.K., Geffen G.M., Martin N.G. Genetic variation of individual alpha frequency (IAF) and alpha power in a large adolescent twin sample. Int. J. Psychophysiol. 2006; 61: 235–243.
van Beijsterveldt C.E., van Baal G.C. Twin and family studies of the human electroencephalogram: a review and a meta-analysis. Biol. Psychol. 2002; 61: 111–138.

qEEG and mental functions
Angelakis E., Stathopoulou S., Frymiare J.L., Green D.L., Lubar J.F., Kounios J. EEG neurofeedback: a brief overview and an example of peak alpha frequency training for cognitive enhancement in the elderly. Clin. Neuropsychol. 2007; 21: 110–129.
Bartrés-Faz D., Arenaza-Urquijo E.M. Structural and functional imaging correlates of cognitive and brain reserve hypotheses in healthy and pathological aging. Brain Topogr. 2011; 24: 340–357.
Basar E. Oscillations in “brain-body-mind” — A holistic view including the autonomous system. Brain Res. 2008; 1235: 2-11.
Basar E. Brain function and oscillations: I. Brain oscillations, principles and approaches. Berlin: Springer; 1998.
Basar E. Brain function and oscillations: II. Integrative brain function. Neurophysiology and Cognitive Processes. Berlin: Springer; 1999.
Cecere R., Rees G., Romei V. Individual differences in alpha frequency drive crossmodal illusory perception. Curr. Biol. 2015; 25: 231–235.
Collura T.F. Neuronal dynamics in relation to normative electroencephalography assessment and training. Biofeedback 2008; 36: 134–139.
Danko S.G., Larisa M. Kachalova L.M., Solovjeva M.L. Differentiation of cognitive-specific states of attention: EEG when verbal memorizing and when recalling. Activ. Nerv. Super. 2013; 55.
Hanslmayr S., Sauseng P., Doppelmayr M., Schabus M., Klimesch, W. Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Appl. Psychophysiol. Biofeedback 2005; 30: 1–10.
Hughes J.R., John E.R. Conventional and quantitative electroencephalography in psychiatry. Neuropsychiatry 1999; 11: 190-208.
Klimesch W. EEG alpha and theta oscillations reﬂect cognitive and memory performance: a review and analysis. Brain Res. Brain Res. Rev. 1999; 29: 169–195.
Knyazev G.G., Slobodskaya H.R. Personality trait of behavioural inhibition is associated with oscillatory systems reciprocal relationships. Int. J. Psychophysiol. 2003; 48: 247–261.
Knyazev G.G., Slobodskaya H.R., Safronova M.V., Sorokin O.V., Goodman R., Wilson G.D. Personality, psychopathology and brain oscillations. Pers. Individ. Dif. 2003; 35: 1331-1349.
Lazarev V.V. The relationship of theory and methodology in EEG studies of mental activity. Int. J. Psychophysiol. 2006; 62: 384–393.
Prichep L., John E., Ferris B., Reisberg B., Alper K., Cancro, R. Quantitative EEG correlates of cognitive deterioration in the elderly. Neurobiol. Aging 1994; 15: 85–90.
Samaha J., Bauer P., Cimaroli S., Postle B.R. Top-down control of the phase of alpha-band oscillations as a mechanism for temporal prediction. Proc. Natl. Acad. Sci. USA 2015; 112: 8439–8444.
Samaha J., Postle B.R. The speed of alpha-band oscillations predicts the temporal resolution of visual perception. Curr. Biol. 2015; 25: 2985–2990.
Steffener J., Stern Y. Exploring the neural basis of cognitive reserve in aging. Biochim. Biophys. Acta 2012; 1822: 467–473.
Takahashi T., Murata T., Hamada T., Omori M., Kosaka H., Kikuchi M., et al. Changes in EEG and autonomic nervous activity during meditation and their association with personality traits. Int. J. Psychophysiol. 2005; 55: 199–207.

Neurometry
Aurlien H., Gjerde I.O., Aarseth J.H., Eldoen G., Karlsen B., Skeidsvoll H., Gilhus N.E. EEG background activity described by a large computerized database. Clin. Neurophysiol. 2004; 115: 665–673.
John E.R. Neurometrics: Clinical Applications of Quantitative Electrophysiology. 1977; Lawrence Erlbaum Associates Publishers: Hillsdale, New Jersey.
John E.R., Karmel B.Z., Corning W.C., Easton P., Brown D., Ahn H., John M., et al. Neurometrics: Numerical taxonomy identifies different profiles of brain functions within groups of behaviorally similar people. Science 1977; 196: 1383–410.
John E.R., Karmel B.Z., Corning, Easton P., Brown D., Ahn H., John M., et al. Neurometrics: Computerized diagnosis and remediation of brain dysfunctions. In: Chacko G.K., ed. Health Handbook. Amsterdam: N. Holland Publishing; 1979: 330–481.
John E.R., Prichep L.S., Chabot R.J. Quantitative electrophysiological maps of mental activity. In: Basar E., Bullock T.H., eds. Dynamics of Sensory and Cognitive Processing by the Brain. Springer-Verlag; 1989.
John E.R., Prichep L.S., Easton P. Normative data banks and Neurometrics: Basic concepts, methods and results of norm construction. In: Gevins A.S., Remond A., eds. Handbook of Electroencephalography and Clinical Neurophysiology, Vol. I. Amsterdam: Elsevier; 1987: 449–495.
John E.R., Prichep L.S., Friedman J., Easton P. Neurometrics: Computer-assisted differential diagnosis of brain dysfunctions. Science 1988; 293: 162–169.