Cline, Christopher2020-08-252020-08-252018-05https://hdl.handle.net/11299/215134University of Minnesota Ph.D. dissertation.May 2018. Major: Biomedical Engineering. Advisor: Bin He. 1 computer file (PDF); xi, 125 pages.Transcranial magnetic stimulation (TMS) and electroencephalography (EEG) provide means to noninvasively measure and modulate activity in the brain. EEG has the potential to infer user intent from measured signals, making it possible to build brain-computer interfaces for augmentative and alternative communication and control of devices that do not rely on intact motor function. TMS offers the ability to transiently perturb neural activity with good temporal and spatial precision, and to modulate longer-term excitability and network function, with various applications in both neuroscientific research and clinical treatment. However, both EEG and TMS have limitations, due in a large part to their noninvasiveness. EEG-based BCIs face issues with inconsistent inference of intent estimated from low-SNR measurements, which degrades the speed and accuracy of BCI control. Likewise, current TMS approaches face issues with variability in responses to stimulation, based on lack of precise targeting information and knowledge of underlying mechanisms of stimulation effects, resulting in inefficient or inconsistently effective clinical neuromodulation interventions. In this work, I describe several efforts to address these issues using approaches combining TMS and EEG. To improve our understanding of factors influencing successful motor imagery based BCI control, I applied TMS targeted at perturbing specific neural circuits and measuring resulting changes in BCI control. Conversely, I also explored factors influencing responses to TMS and how EEG can be used to inform stimulation via measurements of stimulation response and estimation of pre-stimulation brain state.enelectroencephalographyneuroimagingtranscranial magnetic stimulationThesis or Dissertation