Science & Technology

VoxNeuro’s technology provides objective data of real-time cognitive function in a clear report.

What we do

VoxNeuro’s electroencephalography (EEG) protocol and medical software analyze and compare a patient’s event-related potentials (ERPs) to VoxNeuro’s normative database to determine cognitive function scores of memory, information processing, attention & concentration.

How we do it

VoxNeuro’s technology is made up of two proprietary components: the Cognitive Health Assessment™ and our medical software. 

The Cognitive Health Assessment™ follows VoxNeuro’s evidence-based electroencephalography (EEG) protocol. 

The assessment takes 30 minutes to complete and is non-invasive. EEG electrodes sit on the scalp and record the brain’s electrical activity through EEG conductive gel. Patients may think of this as similar to an ultrasound. 

The assessment guides patients through a series of neuropsychological tests on a computer while the EEG records hundreds of thousands of data-points along their brain waves. Among those data points are ERPs (event-related potentials) which indicate the health of core cognitive functions like memory, information processing, attention & concentration.

healthy average waveform vs patient waveform graph
The figure above shows example brain waveforms recorded by VoxNeuro’s Cognitive Health Assessment™. The figure compares the averaged brain responses of neurologically healthy controls (Red) within a specific age range, to that of an individual’s brain responses (Blue) suffering from an undisclosed neurological pathology. The figure demonstrates a reduction in response size to each of the event-related potential (ERP) components, indicating reductions in cognitive ability. The N100 ERP brain biomarker is indicative of auditory and visual processing, the N200 ERP brain biomarker is a measure of cognitive control through conflict monitoring in information processing, and the P300 ERP, a task-dependent brain response, is reflective of attention, concentration, and working memory.

VoxNeuro’s medical software rapidly analyzes, transforms and compares the EEG’s ERP data to VoxNeuro’s database of healthy cognitive function to generate a patient report. The report provides scores of the patient’s core cognitive functions to help support clinical findings and inform clinical decisions by healthcare providers.

To learn more about the science and methodology behind a VoxNeuro Cognitive Health Assessment™ Report, download a sample report through the form below.

For healthcare providers, VoxNeuro helps to elevate brain health evaluation with rapid, objective and actionable data.

VoxNeuro complements clinical protocols to track brain health over time, provide valuable data to the diagnostic process, and help inform customized treatment strategies.

When treatment is undertaken, repeat assessments track changes in a patient’s cognitive scores to monitor their progress and validate the efficacy of treatment, helping healthcare providers to maximize patient outcomes and expedite recovery timelines.

Find out more about how our technology supports healthcare providers, patients and other stakeholders here.

Why we are different

  • Scores multiple core cognitive functions
  • Limitless brain health applications
  • Supports any clinical group
  • Available to anyone 12-90 years of age
  • 30+ years of peer reviewed studies and research
  • Proprietary, evidence-based EEG protocol
  • Proprietary medical software

Request a sample report

Structural vs. Functional Brain Assessment

Solutions for assessing brain health fall drastically behind those for other vital organs, like the heart and lungs. Current neuroimaging technologies, like CT and MRI, are well established to assess structural damage but not functional damage. They rely on indicators like bone damage, lesions and irregular blood flow, not electrical activity. Assessing electrical activity and knowing what the electrical patterns of the brain mean is key to truly understanding how the brain is performing, and what that means for a patient’s functional ability.

VoxNeuro’s Genesis

Dr. John F. Connolly is the Chief Science Officer and co-founder of VoxNeuro Inc. He is the former Senator William McMaster Chair in Cognitive Neuroscience, the founding director of the ARiEAL Research Centre, and co-directs the Language, Memory and Brain Lab. He is an author on over 330 articles*, chapters, and presentations, most of which involve EEG/ERP, MEG and MRI studies of cognitive health and neuropathology.

Dr. Connolly has served as a consultant for a variety of organizations including:

  • Natural Science and Engineering Research Council of Canada (NSERC)
  • Canadian Institutes of Health Research (CIHR)
  • Canada Foundation for Innovation
  • Ontario Brain Institute (OBI)
  • Ontario Centres of Excellence (OCE)
  • Scottish Rite Charitable Foundation (Canada & USA)
  • March of Dimes Research Foundation (USA)
  • National Institutes of Health (USA)
  • Heart and Stroke Foundation of Canada
  • The Hospital for Sick Children (Sick Kids)
  • Networks of Centres of Excellence of Canada
  • Autism Speaks’ High Risk – High Impact Initiatives (USA)
  • Collaborative Health Research Projects (CIHR & NSERC)

Dr. Connolly has also served as a Member of the Editorial Board and subsequently Associate Editor of Clinical Neurophysiology – the flagship journal of the International Federation of Clinical Neurophysiology.

He has served as a consultant to other research and evidence-based policy groups in Australia, New Zealand, Hong Kong, Belgium, Flanders, Finland, and the United Kingdom.

Dr. Connolly is a member of the Scientific Advisory Board for Brain Injury Canada to inform an integrated and comprehensive Brain Injury Resource website. It serves to provide reliable & evidence-based information related to brain injury, and a comprehensive list of existing services for Canadians with brain injuries, their families and caregivers.

*View the Foundational Research section below for a collection of publications and peer reviewed journals.

The scientific breakthrough in cognitive assessment

In 1999, Dr. Connolly was the first person in the world to demonstrate that a patient (mis)diagnosed as being in a vegetative state (now referred to as Unresponsive Wakefulness Syndrome) was actually in a locked-in state, meaning they were fully functioning cognitively but experiencing near-complete physical paralysis. After 9 months of successful cognitive rehabilitation efforts, the patient had regained complete motor control and was discharged, walking himself out of the hospital. This landmark case led to the development of EEG-based methods of assessing conscious states and mental functions in patient populations including autism, Unresponsive Wakefulness Syndrome (vegetative state), coma, and concussion. This evidence based research forms the foundation of VoxNeuro’s technology.

Foundational Research

Watch a summary of the foundational research behind VoxNeuro produced by Scientia:

The written version of the Scientia publication can be read here.

N2b Reflects the Cognitive Changes in Executive Functioning After Concussion: A Scoping Review

Krokhine, S. N., Ewers, N. P., Mangold, K. I., Boshra, R., Lin, C. A., Connolly, J. F (2020)
Frontiers in Human Neuroscience, 14:601370
DOI: 10.3389/fnhum.2020.601370

Neurophysiological markers of cognitive deficits and recovery in concussed adolescents

Ruiter, K. I., Boshra, R., DeMatteo, C., Noseworthy, M., Connolly, J. C. (2020)
Brain Research, 146998
DOI: 10.1016/j.brainres.2020.146998

On the time-course of functional connectivity: theory of a dynamic progression of concussion effects

Boshra, R., Ruiter, K. I., Sonnadara, R., Reilly, J. P., Connolly, J. C. (2020)
Brain Communications, fcaa063
DOI: 10.1093/braincomms/fcaa063

Neurophysiological Correlates of Concussion: Deep Learning for Clinical Assessment

Boshra, R., Ruiter, K. I., DeMatteo, C., Reilly, J. P., Connolly, J. C. (2019)
Nature Scientific Reports, 9 (17341)
DOI: 10.1038/s41598-019-53751-9

From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes

Boshra, R., Dhindsa, K., Boursalie, O., Ruiter, K. I., Sonnadara, R., Samavi, R., Doyle, T. E., Reilly, J. P., Connolly, J. F. (2019)
IEEE, 27(7)
DOI: 10.1109/TNSRE.2019.2922553

Disruption of Function: Neurophysiological markers of cognitive deficits in retired football players

Ruiter, K. I., Boshra, R., Doughty, M., Noseworthy, M., Connolly, J. F. (2019)
Clinical Neurophysiology, 130(1): 111-121
DOI: 10.1016/j.clinph.2018.10.013

Global Aphasia: An innovative assessment approach

Connolly, J. F., Mate-Kole, C., Joyce, B. M. (1999)
Archives of Physical Medicine and Rehabilitation, 80(10): 1309-1315
DOI: 10.1016/S0003-9993(99)90035-7

Detection of brain activation in unresponsive patients with acute brain injury.

Claassen, J., Doyle, K., Matory, A., Couch, C., Burger, K. M., Velazquez, A., Okonkwo, J.U., King, J-R., Park, S., Agarwal, S., Roh, D., Megjhani, M., Eliseyev, A., Connolly, E., Rohaut, B. (2019).
The New England Journal of Medicine, 380(26), 2497-2505.


Linking neurophysiological and neuropsychological measures for aphasia assessment

Marchand, Y., D’Arcy, R., Connolly, J. F. (2002)
Clinical Neurophysiology, 113(11)
DOI: 10.1016/S1388-2457(02)00224-9

Development of a point of care system for automated coma prognosis: a prospective cohort study protocol

Connolly, J. F., Reilly, J. P., Fox-Robichaud, A., Britz, P., Blain-Moraes, S., Sonnadara, R., Hamielec,  C., Herrera-Diaz, A., Boshra, R. (2019)
BMJ Open, 9(7)
DOI: 10.1136/bmjopen-2019-029621

Automatic and continuous assessment of ERPs for mismatch negativity detection

Armanfard, N., Kameili, M., Reilly, J. P., Mah, R., Connolly, J. F. (2016)
DOI: 10.1109/EMBC.2016.7590863

Automatic and continuous assessment of ERPs for mismatch negativity detection

Blain-Moraes, S., Boshra, R., Kan Ma, H., Mah, R., Ruiter, K. I., Avidan, M., Connolly, J. F., Mashour, G. A. (2016)
Frontiers in Human Neuroscience
DOI: 10.1109/EMBC.2016.7590863

Assessment of children’s receptive vocabulary using event related potentials; Development of clinically validated test

Byrne, J. M., Dywan, C. A., Connolly, J. F. (1995).
Child Neuropsychology, 1(3): 211-223
DOI: 10.1080/09297049508400226

Assessing adult receptive vocabulary with event related potentials: An investigation of cross-modal and cross-form priming

Connolly, J. F., Byrne, J. M., Dywan, C. A. (1995)
Journal of Clinical and Experimental Neuropsychology, 17(4)
DOI: 10.1080/01688639508405145

An innovative method to assess the receptive vocabulary of children with cerebral palsy using event-related potentials

Byrne, J. M., Dywan, C. A., Connolly, J. F. (1995)
Journal of Clinical and Experimental Neuropsychology, 17(1): 9-19
DOI: 10.1080/13803399508406576

Brain activity and language assessment using event-related potentials: development of a clinical protocol

Byrne, J. M., Connolly, J. F., MacLean, S. E., Dooley, J. M., Gordon, K. E., Beattie, T. L. (1999)
Developmental Medicine & Clinical Neurology, 41(11)
DOI: 10.1111/j.1469-8749.1999.tb00534

Brain Activity and Cognitive Status in Pediatric Patients: Development of a Clinical Assessment Protocol

Byrne, J. M., Connolly, J. F., MacLean, S. E., Beattie, T. L., Dooley, J. M., Gordon, K. E. (2001)
Journal of Child Neurology, 16(5): 325-332
DOI: 10.1177/088307380101600504

Electrophysiological evidence for the integral nature of tone in Mandarin spoken word recognition

Ho, A., Boshra, R., Schmidtke, D., Oralova, G., Moro, A. L., Service, E., Connolly, J. F. (2019)
Neuropsychologia, 131: 325-332
DOI: 10.1016/j.neuropsychologia.2019.05.031

Supporting Literature

1. Byrne, J.M., Connolly, J.F., McLean, S.E., Beattie, T.L., Dooley, J.M., & Gordon, K.E. (2001). Brain activity and cognitive status in pediatric patients: Development of a clinical assessment protocol. Journal of Child Neurology, 16, 325-332. DOI: 10.1177/088307380101600504

2. Connolly, J.F., D’Arcy, R.C.N., Newman, R.L., & Kemps, R. (2000). The application of cognitive event-related brain potentials (ERPs) in language- impaired individuals: Review and case studies (Invited Review: Ed. Dr. J. Polich). International Journal of Psychophysiology, 38, 55-70.

3. Connolly, J.F. & D’Arcy, R.C.N. (2000). Innovations in neuropsychological assessment using event-related brain potentials (Invited Review). International Journal of Psychophysiology, 37, 31-47. DOI: 10.1016/S0167-8760(00)00093-3

4. D’Arcy, R.C.N. & Connolly, J.F. (1999). An event-related brain potential study of receptive speech comprehension using a modified Token Test. Neuropsychologia, 37, 1477-1489. DOI: 0.1016/S0028-3932(99)00057-3

5. D’Arcy, R.C.N., Connolly, J.F., & Eskes, G. A. (2000). Evaluation of reading comprehension with neuropsychological and event-related brain potential (ERP) methods. Journal of the International Neuropsychological Society, 6, 556-567. LINK:

6. Duncan CC, Barry RJ, Connolly JF, et al. Event-related potentials in clinical research: guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400. Clin Neurophysiol. 2009;120(11):1883–1908.
DOI: 10.1016/j.clinph.2009.07.045

7. Frishkoff, G., Sydes, J., Mueller, K., Frank, R., Curran, T., Connolly, J., Kilborn, K., Molfese, D., Perfetti, C., & Malony, A. (2011). Minimal Information for Neural Electromagnetic Ontologies (MINEMO): A standards- compliant method for analysis and integration of event-related potentials (ERP) data. Standards in Genomic Sciences, 5(2): 211–223. DOI: 10.4056/sigs.2025347

8. Folstein JR, Van Petten C. Influence of cognitive control and mismatch on the N2 component of the ERP: a review. Psychophysiology. 2008;45(1):152–170.

9. Harker, K.T. & Connolly, J.F. (2007). Assessment of visual working memory using event-related potentials. Clinical Neurophysiology, 118, 2479–2488.
DOI: 10.1016/j.clinph.2007.07.026

10. Heil M, Osman A, Wiegelmann J, Rolke B, Hennighausen E. N200 in the Eriksen-task: Inhibitory executive process? J Psychophysiol. 2000;14(4):218–218.

11. Kok A. On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology. 2001;38(3):557–577.

12. Kutas M, McCarthy G, Donchin E. Augmenting mental chronometry: the P300 as a measure of stimulus evaluation time. Science. 1977;197(4305):792–795.

13. Lefebvre, C.D., Marchand, Y., Eskes, G.A., & Connolly, J.F. (2005). Assessment of working memory abilities using an event-related brain potential (ERP)-compatible digit span backward task. Clinical Neurophysiology, 116, 1665-1680.
DOI: 10.1016/j.clinph.2005.03.015

14. Light GA, Swerdlow NR, Braff DL. Preattentive sensory processing as indexed by the MMN and P3a brain responses is associated with cognitive and psychosocial functioning in healthy adults. J Cogn Neurosci. 2007;19(10):1624–1632.

15. Lim CL, Gordon E, Rennie C, et al. Dynamics of SCR, EEG, and ERP activity in an oddball paradigm with short interstimulus intervals. Psychophysiology. 1999;36(5):543–551.

16. Marchand, Y., Lefebvre, C., & Connolly, J.F. (2006). Correlating digit span performance and event-related potentials to assess working memory. International Journal of Psychophysiology, 62, 280-289. DOI: 10.1016/j.ijpsycho.2006.05.007

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18. Näätänen R, Gaillard AWK, Mäntysalo S. Early selective-attention effect on evoked potential reinterpreted. Acta Psychol (Amst). 1978;42(4):313–329.

19. Näätänen R, Paavilainen P, Rinne T, Alho K. The mismatch negativity (MMN) in basic research of central auditory processing: a review. Clinical Neurophysiology.

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6. De Beaumont L, Beauchemin M, Beaulieu C, Jolicoeur P. Long-term attenuated electrophysiological response to errors following multiple sports concussions.
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10. Gawryluk, J.R., D’Arcy, R.C.N., Connolly, J.F., & Weaver, D.F. (2010). Improving the clinical assessment of consciousness with advances in electrophysiological and neuroimaging techniques.
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13. Harrison, A. & Connolly, J.F. (2013). Finding a way in: a review and practical evaluation of fMRI and EEG in disorders of consciousness.
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1. ArmanFard, N., Komeili, M., Reilly, J. & Connolly, J. (2018). A machine learning framework for automatic and continuous MMN detection with preliminary results for coma outcome prediction. IEEE Journal of Biomedical and Health Informatics [Epud ahead of print].
DOI: 10.1109/JBHI.2018.2877738.

2. Blain-Moraes, S., Boshra, R., Ma, H.K., Mah, R., Ruiter, K., Avidan, M., Connolly, J.F. and Mashour, G.A. (2016). Normal brain response to propofol in advance of recovery from unresponsive wakefulness syndrome. Frontiers in Human Neuroscience. 2016 June 2; 10:248.
DOI: 10.3389/fnhum.2016.00248

3. Claassen, J., Doyle, K., Matory, A., Couch, C., Burger, K. M., Velazquez, A., Okonkwo, J. U., King, J-R., Park, S., Agarwal, S., Roh, D., Megjhani, M., Eliseyev, A., Connolly, E., Rohaut, B. (2019). Detection of brain activation in unresponsive patients with acute brain injury.
The New England Journal of Medicine, 380(26), 2497-2505. DOI: 10.1056/NEJMoa1812757

4. Connolly, J.F., Mate-Kole, C.C., & Joyce, B.M. (1999). Global aphasia: An innovative assessment approach.
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5. Connolly, J.F., Major, A., Allen, S., & D’Arcy, R.C.N. (1999). Performance on WISC-III and WAIS-R NI vocabulary subtests assessed with event-related brain potentials: An innovative method of assessment.
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6. Connolly, J.F, Marchand, Y., Major, A., & D’Arcy, R.C.N. (2006). Event- related brain potentials as a measure of performance on WISC-III and WAIS- R NI Similarities sub-tests.
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8. Mah, R., & Connolly, J. (2018). A framework for the extended monitoring of levels of cognitive function in unresponsive patients. PLoS ONE, 13(7): e0200793.
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9. Morlet D, Fischer C. MMN and novelty P3 in coma and other altered states of consciousness: a review.
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10. Wang, J.T., Young, G.B., & Connolly, J.F. (2004). Prognostic value of evoked responses and event-related brain potentials in coma.
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11. Young, G.B., Wang, J.T., & Connolly, J.F. (2004). Prognostic determination in anoxic-ischemic and traumatic encephalopathies.
Journal of Clinical Neurophysiology, 21, 379-390. Link:

1. D’Arcy, R.C.N., Marchand, Y., Eskes, G.A., Harrison, E.R., Phillips, S. J., Major, A., & Connolly, J.F. (2003).
Electrophysiological assessment of language function following stroke. Clinical Neurophysiology, 114, 662-672. DOI: 10.1016/S1388-2457(03)00007-5

1. Dick, B.D., Connolly, J.F., McGrath, P.J., Finley, G.A., Stroink, G., Houlihan, M.E., & Clark, A.J. (2003). The disruptive effect of chronic pain on mismatch negativity.
Clinical Neurophysiology, 114, 1497-1506. DOI: 10.1016/S1388-2457(03)00133-0

2. Dick, B.D., Connolly, J.F., Houlihan, M.E., McGrath, P.J., Finley, G.A., Stroink, G., & Clark, A. J. (2006). Effects of experimental pain on Mismatch Negativity.
Journal of Psychophysiology, 20(1), 21-31. DOI: 10.1027/0269-8803.20.1.21

3. Houlihan, M.E., McGrath, P.J., Connolly, J.F., Stroink, G., Finley G.A., Dick, B., & Phi, T.T. (2004). Assessing the effect of pain on demands for attentional resources using ERPs.
International Journal of Psychophysiology, 51, 181-187. DOI: 10.1016/j.ijpsycho.2003.08.001

4. Troche, S.J., Houlihan, M.E., Connolly, J.F., Dick, B.D., McGrath, P.J., Finley, G.A. and Stroink, G. (2014). The effect of pain on involuntary and voluntary capture of attention.
European Journal of Pain, Article first published online: 23 JUL 2014 | DOI: 10.1002/ejp.553