Rober Boshra, Ph.D.

Rober Boshra, Ph.D.

Researcher of Neurophysiology and Machine Learning

Biography

Rober Boshra is an active researcher in neurophysiology and the application of machine learning in the clinical setting to improve assessment and outcome. His work in brain injury targeted objective concussion assessment using EEG/ERP as well as investigated automated detection of markers of consciousness in coma and vegetative state. He has interdisciplinary training as a computer scientist, neuroscientist, and biomedical engineer. Rober is an alumnus of McMaster’s AI-focused MacData institute and an active affiliate of Toronto’s world-renowned Vector Institute of AI. Rober is the Director of AI and Technology at VoxNeuro and is currently leading a project with funding awarded by the National Research Council to scale the company’s services across Canada.

Interests

  • Artificial Intelligence & Machine Learning
  • Neuroscience
  • Brain Injury

Education

  • Ph.D. in Biomedical Engineering, 2019

    McMaster University

  • M.Sc. in Neuroscience, 2016

    McMaster University

  • B.Sc. in Computer Science, 2013

    Dalhousie University

Skills

Machine Learning

Brain Imaging

Python

R

Statistics

Linux

Experience

 
 
 
 
 

Director of AI & Technology

VoxNeuro Inc.

Oct 2017 – Present Hamilton, ON
Responsibilities include:

  • Managing SaaS development
  • Developing technology infrastructure
  • Adaptation of scientific findings
 
 
 
 
 

Machine Learning Engineer

Healthcare Innovation in NeuroTechnology

Sep 2016 – Jan 2018 Hamilton, ON
 
 
 
 
 

Teaching and Research Assistant

McMaster University

Sep 2014 – Jul 2019 Hamilton, ON
 
 
 
 
 

Teaching and Research Assistant

Dalhousie University

Sep 2011 – Dec 2013 Halifax, NS

Recent & Upcoming Talks

Electrophysiological markers of cognitive dysfunction in brain injury and machine learning methods of revealing them

This presentation will provide an overview of our group’s work using electrophysiological measures (i.e. electroencephalography (EEG) and event-related potentials (ERP)) of brain activity to study individuals who have sustained a brain injury. In order to examine the consequences of brain injury, we have taken advantage of the component structure of ERPs in which particular time-based features of brain responses to stimulus environments reflect functions such as attention, memory, and language comprehension. Work will be described demonstrating the value of this approach in assessing functional capacity in a variety of patients including those who have been diagnosed as being in a vegetative state, in a coma, or having sustained a concussion. The more traditional approaches to signal processing will be discussed followed by a presentation of the significant “value-added” in using machine learning (ML) methods to provide more fine-grained assessment of the neurophysiological signals obtained in these different patient populations. The talk will discuss a number of different approaches that were taken to apply ML to these types of biological signals, challenges that particularly affect these applications (e.g., limited sample sizes, skewed classes, and varying SNRs), and strategies our group has taken to bridge that gap in the domain of clinical EEG/ERP.

A brief history of electrophysiological research on brain injury and recent advances enabled by machine learning

This presentation will provide an overview of our group’s work using electrophysiological measures (i.e. electroencephalography (EEG) and event-related potentials (ERP)) of brain activity to study individuals who have sustained a brain injury. In order to examine the. consequences of brain injury, we have taken advantage of the component structure of ERPs in which particular time-based features of brain responses to stimulus environments reflect functions such as attention, memory, and language comprehension. Work will be described demonstrating the value of this approach in assessing functional capacity in a variety of patients including those who have been diagnosed as being in a vegetative state or in a coma, or those diagnosed with a concussion. The more traditional approaches to signal processing will be discussed followed by a presentation of the significant “value-added” in using machine learning (ML) methods to provide more fine-grained assessment of the neurophysiological signals obtained in these different patient populations. The talk will discuss a number of different approaches that were taken to apply ML to these types of biological signals, challenges that particularly affect these applications (e.g., limited sample sizes, skewed classes, and varying SNRs), and strategies our group has taken to bridge that gap in the domain of clinical EEG/ERP.

AI Panel in Healthcare & Future

Publications

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On the time-course of functional connectivity: theory of a dynamic progression of concussion effects
Development of a point of care system for automated coma prognosis: A prospective cohort study protocol
Disruption of function: Neurophysiological markers of cognitive deficits in retired football players
Electrophysiological evidence for the integral nature of tone in Mandarin spoken word recognition
From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes