Available at: https://digitalcommons.calpoly.edu/theses/1822
Date of Award
MS in Biomedical Engineering
Biomedical and General Engineering
Movement disorders are a group of syndromes that often arise due to neurological abnormalities. Approximately 40 million Americans are affected by some form of movement disorder, significantly impacting patients’ quality of life and their ability to live independently. Deep brain stimulation (DBS) is one treatment that has shown promising results in the past couple decades, however, the currently used open-loop system has several drawbacks. By implementing a closed-loop or adaptive DBS (aDBS) system, the need for expensive parameter reprogramming sessions would be reduced, side-effects may be relieved, and habituation could be avoided. Several biomarkers, for example signals or activity derived from electroencephalogram (EEG), could potentially be used as a feedback source for aDBS. Here, we attempted to characterize cortical EEG potentials in healthy subjects performing six tasks that are difficult for those with movement disorders. Using a 32-channel EEG cap with an amplifier sampling at 500 Hz, we performed our protocol on 11 college-aged volunteers lacking any known movement disorder. For each task, we analyzed task-related power (TRP) changes, spectrograms, and topographical maps. In a finger movement exercise, we found task-related depression (TRD) in the delta band at the F4 electrode, as well as TRD at the C3 electrode in the alpha band during a pencil-pickup task, and TRD at the F3 electrode in the beta band during voluntary swallowing. While delta-ERD in the finger movement exercise was likely due to ocular artifact, the other significant results were in line with what relevant literature would predict. The findings from the work, in conjunction with a future study involving movement disorder patients, can provide insight into the use of EEG as a feedback source for aDBS.
Keywords: EEG, electroencephalography, neurostimulation, deep brain stimulation, movement disorders, closed-loop DBS, adaptive DBS, aDBS
Available for download on Wednesday, December 02, 2020