Combining non-invasive brain stimulation with machine learning techniques to predict cognitive-sensorimotor interactions during skilled behaviours in healthy and clinical populations

The project aims to enhance our understanding and ability to predict the dynamics of cognitive and sensorimotor functions in skilled behaviors. Through the integration of non-invasive brain stimulation (like TMS) and machine learning algorithms, this research seeks to develop models that can foresee cognitive-sensorimotor interaction outcomes both in individuals with neurological conditions and healthy subjects. It involves collecting data on brain activity using non-invasive techniques while participants engage in predefined tasks. This data will then be processed and analyzed using sophisticated machine learning models to identify predictive markers of performance and recovery potential in clinical populations. These markers can provide invaluable insights into the design of personalized rehabilitation programs, enhancing the efficacy of treatments for neurological conditions such as stroke or traumatic brain injury.
The expected outcome of this project is a report describing a method that can be used to develop better diagnostic tools, rehabilitation therapies, ultimately improving people’s lives.

Faculty Supervisor:

Sean Meehan

Student:

Partner:

Lviv Polytechnic National University

Discipline:

Computer science

Sector:

Artificial Intelligence; Health and Related Sciences & Technology

University:

University of Waterloo

Program: