Year of Publication


Degree Name

Master of Science in Biomedical Engineering

Document Type

Master's Thesis




Biomedical Engineering

First Advisor

Dr. Sridhar Sunderam


Impaired motor function following neurological injury may be overcome through therapies that induce neuroplastic changes in the brain. Therapeutic methods include repetitive exercises that promote use-dependent plasticity (UDP), the benefit of which may be increased by first administering peripheral nerve stimulation (PNS) to activate afferent fibers, resulting in increased cortical excitability. We speculate that PNS delivered only in response to attempted movement would induce timing-dependent plasticity (TDP), a mechanism essential to normal motor learning. Here we develop a brain-machine interface (BMI) to detect movement intent and effort in healthy volunteers (n=5) from their electroencephalogram (EEG). This could be used in the future to promote TDP by triggering PNS in response to a patient’s level of effort in a motor task. Linear classifiers were used to predict state (rest, sham, right, left) based on EEG variables in a handgrip task and to determine between three levels of force applied. Mean classification accuracy with out-of-sample data was 54% (23-73%) for tasks and 44% (21-65%) for force. There was a slight but significant correlation (p<0.001) between sample entropy and force exerted. The results indicate the feasibility of applying PNS in response to motor intent detected from the brain.