fNIRS prototype vs. gold standard comparison study

We are testing the performance of a prototype wireless functional near-infrared spectroscopy (fNIRS) device developed by Axem Neurotechnology (startup founded by LBRF lab members Chris Friesen and Tony Ingram) against an established fNIRS research system. Specifically, we’ll be looking to determine how these devices compare in their ability to measure the cerebral hemodynamic response to simple upper- and lower-extremity movements respectively.

Project members: Chris Friesen, Tony Ingram

Neurofeedback-based stroke rehabilitation at home

We are testing the feasibility of having stroke survivors perform upper-extremity rehabilitation at home using a rehabilitation system comprised of a tablet and headband that measures brain activity using functional near-infrared spectroscopy (fNIRS). This study will examine the feasibility of having stroke survivors perform neurofeedback-based rehabilitation independently in the comfort of their own home. The study will also examine the ability of a prototype fNIRS device developed by Axem Neurotechnology (startup founded by LBRF lab members Chris Friesen and Tony Ingram) to detect known biomarkers of post-stroke motor recovery based on the data collected during rehabilitation sessions.

Project members: Chris Friesen, Tony Ingram

Learning without doing. Error detection and correction in motor imagery-based learning

Motor imagery (MI) can help us learn new or improve motor skills without actually moving. In typical motor learning, information for the outcome of a movement (i.e. we reach for a coffee cup and knock it off the table) is compared against our plan for the given movement (i.e. how we decided to move our arm to grab the coffee cup). When this information is compared, we can adjust our motor plan to make our movement more accurate. However, in MI, we don’t move and therefore there is no way to compare our motor plan against the outcome of a movement. The goal of this study is to determine how we learn in MI by making it harder to use a brain area known to be involved in MI. The results of this study will help us better understand which parts of the brain are involved in error detection and correction MI based motor learning.

Project members:  Jack Solomon, Darby Green, Amy MacDonald

Seeing is not always believing. Investigating the content of motor imagery-based learning using illusions

Motor imagery (MI) allows us to learn new or improve upon existing motor skills, however the content of what is being learnt is unclear. Visual illusions have been used in the motor execution domain to dissociate visual control of movement into two streams, vision for perception and vision for action. In this context, participant’s perception reflects the illusion but within a few trials, their motor performance “solves” the illusion without participants being aware of this change. However, these illusions are solved in different ways. Some require kinematic feedback from the movement whereas others only require perceptual feedback about the movement performed. The goal of this project is to employ two illusions, one that is solved perceptually and one that requires kinematic feedback to solve, to characterize changes in performance on these illusions after MI training. From the results of this study we can make inferences about what type of feedback is being simulated during MI training (perceptual vs kinematic).

Project members: Jack Solomon, Wenyi Lyu