Edelman, Bradley2020-05-042020-05-042018-02https://hdl.handle.net/11299/213118University of Minnesota Ph.D. dissertation. February 2018. Major: Biomedical Engineering. Advisor: Bin He. 1 computer file (PDF); x, 138 pages.Detecting mental intent through brain-computer interface (BCI) technology has significantly improved the lives of patients suffering from various neurological disorders such as amyotrophic lateral sclerosis and spinal cord injury. BCIs utilizing intracortical signals have achieved closed-loop control of robotic devices for completing everyday actions by performing various motor imagery (MI) tasks. However, the substantial cost and high risk of electrode implantation limits the widespread use of these systems in clinical and recreational settings. A noninvasive counterpart using electroencephalography (EEG) attaining similar levels of performance would profoundly impact the translation of these systems towards everyday life. Nevertheless, ineffective training protocols and poor signal quality significantly hinders EEG-based neural decoding. Here, I present a unique framework for driving EEG BCI towards everyday use, leveraging on (1) increasing cognitive arousal with a novel task paradigm and (2) improving neural decoding through noninvasive neuroimaging techniques. I developed an online continuous tracking task and demonstrated that healthy human users can achieve scalp-recorded neural control competitive with that of invasive work reported in literature. Through enhancing user engagement, this task additionally proved to be a more effective training tool than traditional center-out tasks for driving BCI skill acquisition and the physiological correlates thereof. I additionally reveal the utility of electrical source imaging (ESI), an imaging technique used to reconstruct cortical activity, for significantly improving both offline and online EEG-based neural decoding. Notably, real-time ESI-based control facilitated a profound improvement over scalp control for online CP BCI control in naïve and experienced users. Finally, I created a multimodal BCI using MI and visual attention tasks to test users’ ability to multitask during BCI control as would be needed in practical situations. In all, this thesis presents an encompassing introduction and evaluation of novel EEG BCI techniques that may help bring the technology to daily life.enBrain-Computer InterfaceEEG Source ImagingElectroencephalographyMotor ImageryNeuroimagingSensorimotor RhythmThesis or Dissertation