BrainScope’s technology is based on the premise that the brain’s electrical activity, whether reflexive, automatic, unconscious or conscious, is electrochemical in nature, and has mathematically predictable electrical correlates. 

BrainScope Technology Core Competencies 

EEG   see more
Database   see more
Algorithms   see more
Software   see more
Hardware   see more

see more

see more

see more

see more


Through a series of electro-chemical reactions, mediated by molecules called neurotransmitters, electrical potentials (voltages) are generated and transmitted throughout the brain, traveling continuously between and among the myriad of neurons. This activity establishes the basic electrical signatures of the traditional electroencephalogram (EEG) and creates identifiable frequencies based on structure and function and changing predictably as a function of age.

Quantification and analysis of these features provide EEG-based objective descriptors of brain function. Characterization of the EEG as either being within or beyond normal limits is the core capability to assess normal and abnormal brain function, and specifically identify TBI.

BrainScope’s novel application of advanced mathematics and miniaturized hardware is designed to bridge the limitations of traditional EEG tools (bulky, expensive, need expert data interpretation), to provide easy-to-use, non-invasive, timely, point-of-care tools that can assist with an initial assessment of brain function as well as provide adjunctive assessment across the continuum of brain care. BrainScope’s devices are focused on TBI in military, sports, and emergency/urgent care environments both in the U.S. and internationally.

More about BrainScope:



Long History of Measurement of Brain Electrical Activity
Utilizing EEG Technology

  • EEG was first recorded in humans by the German physiologist and psychiatrist Hans Berger in 1924, whose hope it was that EEG would revolutionize diagnosis and treatment of neurological and psychiatric disorders.

  • It was not until decades later with the advent of computerized methods for signal processing of brain electrical signals that the clinical utility of EEG was advanced.

  • The clinical utility of EEG technology has particularly advanced in recent years given the convergence of:
    ̶ Advanced signal processing methods
    ̶ Leveraging of machine learning and “big data”


Brain Electrical Activity and TBI

  • The pathophysiology of TBI is complex and related to many different aspects of brain function, including neurometabolic, neurophysiological and structural changes in the brain.

  • An extensive scientific literature demonstrates these changes using functional and structural neuroimaging (e.g., MRI, fMRI, PET, SPECT, DTI).

  • Quantitative features of brain electrical activity (QEEG) used in the BrainScope technology have also been shown to be sensitive to these changes in brain activity, without the limitations of neuroimaging tools (e.g., availability at point of care, radiation exposure, cost-effectiveness).

  • For example, the hypometabolism reported in PET imaging in TBI is consistent with slowing of the EEG spectra seen in this population.

  • Changes in connectivity reported in TBI using Diffusion Tensor Imaging (DTI) are consistent with the phase synchrony abnormalities reported using EEG.

  • These changes are captured in the Ahead® system as a profile or pattern of brain activity distinctive of TBI (biomarker) through the application of advanced signal processing methods.


Machine Learning Leveraged to Continuously Enrich
Database and Enhance Algorithms

  • The innovative sophisticated methodologies used in developing the Ahead® classification systems include those that have their foundation in big data and machine learning from genomics, neuroimaging and proteomics.

  • At the core of these is information derived from brain electrical activity allowing the objective quantification of biomarkers of traumatic brain injury.

  • In order to build a robust, validated classification algorithm, careful attention is paid to data reduction prior to selecting candidate features for algorithm development.

  • The binary discriminant classification functions were derived using methods including genetic algorithms, a form of evolutionary algorithms.

  • Evolutionary algorithms perform a stochastic search and evaluate a series of candidate solutions, where each new candidate is informed by high-performing predecessors, similar to genetic evolution.

  • The final classifier functions consist of weighted combinations of selected linear and nonlinear features that reflect brain electrical activity which mathematically describes traumatic structural brain injury as distinguished from normal or concussed brain activity.

  • Details on the development and performance of the algorithms have been published in the peer- reviewed literature.


Sophisticated Algorithms Utilized in Classification Systems

  • Brain electrical activity (EEG) is acquired using the Ahead® hand-held device from frontal regions on the forehead.

  • The importance of removing “noise” (non-EEG signals or artifact) from the brain activity recording is very important. BrainScope uses sophisticated proprietary algorithms for artifact detection and removal.

  • The characteristics of the brain activity are extracted and quantified using advanced signal processing methods and represent the features or biomarkers which are then used as inputs to the algorithms.

  • The innovative sophisticated methodologies used in developing the Ahead® classification systems include those that have their foundation in big data and machine learning from genomics, neuroimaging and proteomics.

  • Leveraging machine learning, there is a continuous feedback loop between enhancement of the database and enhancement of the algorithms.

  • Customizable, easy-to-use software platform.

  • BrainScope’s software is designed from the ground-up to be customizable, efficient, and easy-to-use.

  • Running on the popular Android OS, BrainScope’s software can be adapted to a variety of processing and display devices such as smart phones and tablets.

  • BrainScope’s Graphical User Interface efficiently guides the user through the steps required to collect EEG and any other data utilized by the embedded classification algorithm. By following a rigorous usability engineering process, BrainScope ensures that all aspects of the software, from the “look and feel” to work flow and performance exceed user expectations.


High Performance, Cost-Effective Hardware

  • BrainScope’s proprietary, single use, disposable electrode headsets are designed to facilitate the positioning and application of the electrodes, enabling the assessment process to begin in minutes and minimizing the training required for use.