Brain Modeling & Analysis Research Company
Advanced brainwave analysis
for digital health
What We Do
BrainMARC has developed a breakthrough mobile health platform for monitoring mental activity. Its easy-to-use tool derives proven and robust biomarkers from EEG sampled via two (dry) electrodes only.
BrainMARC’s biomarkers are applicable to a wide spectrum of neuropsychiatric syndromes where they can enhance therapeutic protocols, predict clinical dynamics, and promote general wellness – They are also suitable for non-medical and research applications.
Yael Rozen, Co-founder and CEO
Dr. Rozen is a seasoned life sciences manager and entrepreneur who, for many years, consulted to the medical device industry on technology transfer, business development, clinical trial management and biomed entrepreneur education. Prior to joining BrainMARC, Dr. Rozen was head of the Research and Development unit at the Rambam Medical Centre (Northern Israel’s largest hospital), as well as head of the tech transfer life science activities of the Technion-Israel Institute of Technology. Dr. Rozen holds a BSc in agriculture, an MSc in biotechnology, an MBA in financing and a PhD in environmental microbiology, all from the Hebrew University of Jerusalem.
Goded Shahaf (MD, PhD – Brain research), Co-founder and Chief Scientist
Dr. Shahaf is a brain researcher (PhD, post-doc at the Technion, Israel; currently co-heads the rehabilitative psychobiology lab at Reuth rehabilitation hospital, Israel), physician (MD at the Technion, Israel and clinical practice at Bnei-Zion and Rambam hospitals, Israel) and entrepreneur (co-founder of Elminda LTD). Also possesses academic degrees in mathematics and computer science (Ben-Gurion University) and in psychology (Bar-Ilan University). His expertise and life-project is the neurophysiologic modelling of functional and dysfunctional behavior and advanced analysis of its manifestation in the electrophysiological signal. BrainMARC implements this breakthrough research.
The novel technology developed by BrainMARC is based on many years of multi-disciplinary research. The result is a series of remarkably reliable, science-based indices that are extracted from a very short EEG sampling derived from two (dry) electrodes.
BrainMARC provides – online and mobile – the following concise and reliable indices:
- The Brain Engagement Index (BEI): a marker for the level of sustained attention
- The Focus Events Index (FEI): a marker of ongoing attention recruitments and their precise timing (~1-second resolution)
- Comfort Index (CI): a basic index of affect level
- Comfort Tendency Index (CTI): an index of consistent tendencies in the affect level (increase or decrease). There are also two indices derived from the CTI:
- Media Engagement Index (MEI): the magnitude of the tendency evoked by a media clip
- Media Affect Index (MAI): the direction (+/-) of affect evoked by a media clip
The Science Behind BrainMARC
Extracting effective markers for attention and affect
Reduction of number of electrodes
BrainMARC’s origins are in published academic work done on the analysis of the activity of in-vitro neuronal networks sampled via tens of electrodes, which led to the development of a sophisticated algorithm for the analysis of the flow of activity in such networks. The algorithm thoroughly identifies pathways in the network in response to stimuli, without losing significant information.
After devising a method to discretize the electrophysiological waves to single spike-like events without significant loss of information this network analysis algorithm was applied to averaged event-related potentials (ERP) of EEG (Shahaf et al, 2012). It was noted that the networks emerging from this analysis could be summarized as a superposition of a very small number of processes, which are spread out in space and in time (Shahaf et al, 2015).
In order to estimate the origins of the sampled EEG activity a complex simulation tool was developed that models the flow of information among functional brain regions. Based on the simulation results it is possible to deduce that indeed only a small number of underlying neurophysiological processes affect the EEG signal and the spatiotemporal spread of these underlying processes makes it possible to sample them from a small number of EEG channels (Shahaf et Pratt, 2013). In the case of BrainMARC, which focuses on markers for attention-related processes, we found that a single channel (1 target electrode + 1 reference electrode) is sufficient.
In light of these published advances in EEG analysis as well as the simplification of and consequent cost reduction in EEG hardware, BrainMARC developed its core technology and its platform for mental health monitoring and prediction products for the consumer and the medical markets.
Reduction of needed number of stimuli (sampling time) and extraction of markers from the raw EEG signal
BrainMARC’s marker for attention in averaged ERP activity has a simple envelope that enables the identification of its correlates from single-trial ERPs and from continuous EEG sampling in a manner that is robust to noise and therefore extractable from simpler EEG systems.
Using the markers to guide treatment and predict dynamics
Attention-related biomarkers tend to deviate (either increase or decrease) in various neuropsychiatric dysfunctions such as migraine, major depression, bipolar disorder, schizophrenia, anxiety disorders, ADHD, dementia, etc. The markers also respond to treatments, pharmaceutical or otherwise, in a manner that is correlative to clinical improvement. In fact, BrainMARC’s recent findings consistently demonstrated that, for various neuropsychiatric dysfunctions, the change in the electrophysiological attention-related markers precedes the overt clinical dynamics (e.g., in ADHD on the scale of hours, in migraine on the scale of days, in depression on the scale of weeks and in dementia on the scale of months).
What could be the reason for the high sensitivity of EEG attention markers to clinical dynamics? We believe the answer is that each neuropsychiatric condition serves as a unique (dysfunctional) response to stress. We employ attention processes in order to resolve the stress that we encounter. When productive solutions are not possible, however, dysfunctional ones evolve in order to reach the same goal of ongoing stress reduction. These dysfunctional stress reduction solutions also reduce the attention recruitment that BrainMARC measures (Shahaf, 2015). This is reminiscent of Hull’s drive reduction theory, which is easily implemented in any nervous system (Shahaf et Marom, 2001). Thus it is possible to consider any psychopathology or other stress-related disorder (e.g., migraine) as an ongoing mechanism for stress/drive reduction, which is self-perpetuating. It is possible to postulate a neurophysiological model that can explain these various maladaptive solutions and can effectively monitor attention in order to direct treatment across a vast range of psychopathologies and stress-related disorders.
The first mobile health tools that BrainMARC has developed, based on its platform technology, is a BrainMARC Monitor, which measures attention and affect-related biomarkers. BrainMARC Monitor software works in conjunction with a simple, off-the-shelf, 2-electrode EEG headset.
BrainMARC Monitor (BMM)
The BMM uses the Brain Engagement and Focus Events indices to provide real-time feedback regarding effectiveness of exercise / treatment-whether pharmaceutical, electromagnetic, behavioral or other (e.g., lifestyle modification) - for a wide variety of dysfunctions such as ADHD, migraine, anxiety disorders, depression, bipolar disorder, schizophrenia and dementia.
BMM also uses Comfort and Comfort Tendency indices to provide real-time feedback regarding levels of stress, interest/memorization and/or enjoyment. It can be used to assess the effectiveness of stress reduction treatments for dysfunctions such as anxiety and migraine, as well as to evaluate viewer responses to media clips.
BrainMARC Monitor User Guide
BrainMARC’s biomarker platform is applicable throughout the neuropsychiatric spectrum. Some leading examples, for which we have substantial evidence, are described below.
Based on the dynamic biomarker a mobile phone app for migraine management is in development. The app can predict a migraine attack 1-2 days prior to the attack, and preventative actions can be suggested based on personal user information as well as on general knowledge about what commonly triggers migraine attacks. In addition, the performance biomarker may provide real-time, online evaluation of stress level and thus facilitate tuning and personal optimization of relaxation and other methods typically used in migraine therapy.
The performance biomarker may provide real-time, online evaluation of current ADHD status, as well as predict the clinical dynamics of the disorder over the coming hours. As such, the BrainMARC monitor can be an important adjunct to corrective teaching sessions, can optimize task scheduling, and can support evidence-based decisions regarding the need for or level of pharmaceutical ADHD treatment.
The performance biomarker may provide real-time, online feedback regarding the effectiveness of electromagnetic (TMS) and other treatments used for depression. Because the dynamic biomarker can predict the clinical dynamics of depression over the coming weeks, it can provide an early indication of treatment efficacy or can alert the patient and/or caregiver about impending deterioration.
Positive clinical outcomes have been shown to be correlated to effective recruitment of attention during rehabilitation activities. The performance biomarker may provide real-time, online feedback about user engagement during physical and cognitive rehabilitation sessions. This feedback, when used by the caregiver to adjust the exercises during the session, can dramatically improve the clinical outcome of the session. The value of BrainMARC’s performance monitor (using an off-the-shelf, 2-sensor EEG headset) for rehabilitation guidance was demonstrated in a clinical trial of tens of subjects (post-stroke and control) (Bartur et al, 2015).
The BrainMARC Comfort Monitor was used in a study for the evaluation of media content and was found to be effective in online identification of focus events within seconds, as well as engagement and comfort (enjoyment) in response to specific media clips. These objective markers of viewer engagement with media content could be of great value in both commercial and research applications.
Goded Shahaf, Novel Non-Invasive Methods of Cortical Mapping. (2016). The annual meeting of the American Clinical Neurophysiology Society (ACNS). An Audio presentation.
Reduction of the needed number of electrodes to a single channel
- Shahaf, G., Reches, A., Pinchuk, N., Fisher, T., Bashat, G. B., Kanter, Tauber I, Kerem D, Laufer I, Aharon-Peretz J, Pratt H, & Geva, A. B. (2012). Introducing a novel approach of network oriented analysis of ERPs, demonstrated on adult attention deficit hyperactivity disorder. Clinical Neurophysiology, 123(8), 1568-1580.
- Shahaf, G., & Pratt, H. (2013). Thorough specification of the neurophysiologic processes underlying behavior and of their manifestation in EEG–demonstration with the go/no-go task. Frontiers in human neuroscience, 7.
- Shahaf, G., Fisher, T., Aharon-Peretz, J., & Pratt, H. (2015). Comprehensive analysis suggests simple processes underlying EEG/ERP–Demonstration with the go/no-go paradigm in ADHD. Journal of neuroscience methods, 239, 183-193.
The clinical importance of monitoring attention
- Shahaf, G. (2016). Migraine as dysfunctional drive reduction: insight from electrophysiology. Medical Hypotheses. 91: 62-66.
- Shahaf, G. (2016). A Possible Common Neurophysiologic Basis for MDD, Bipolar Disorder, and Schizophrenia: Lessons from Electrophysiology. Front. Psychiatry.
Use of markers to guide treatment (neural rehabilitation)
- Bartur G, Joubran K, Shani-Peleg S, Vatine J.J., & Shahaf G. Engagement, its relevance to stroke recovery and neurorehabilitation: An applicative on-line EEG tool for enhancing treatment efficacy. Submitted.
- Bartur G, Joubran K, Shani-Peleg S, Vatine J.J., & Shahaf G. An applicative on-line EEG tool for enhancing treatment efficacy in the rehabilitation setting. Rehab science & technology Updates. February 7-10 2016. Poster presentation.
Use of marker for early detection of anti depressive treatment
- Shahaf G., Yariv S., Bloch B., Nitzan U., Segev A., Reshef A., & Bloch Y. (2017). A pilot study of possible easy-to-use electrophysiological index for early detection of anti depressive treatment non-response. Front. Psychiatry, 18 July 2017.