Stieger, James2021-01-252021-01-252020-11https://hdl.handle.net/11299/218058University of Minnesota Ph.D. dissertation. November 2020. Major: Biomedical Engineering. Advisors: Bin He, Stephen Engel. 1 computer file (PDF); 88 pages.Brain computer interfaces (BCIs) are assistive devices that provide individuals with paralysis access to the world. Through decoding brain data in real-time, BCIs can translate user intent into actionable commands that can control computer cursors, wheelchairs, and robotic arms. However, many individuals struggle to learn how to control these devices. In this investigation, we explore two methods to improve brain computer interface performance. First, mind-body awareness training was shown to enhance BCI skill through increasing control over alpha band EEG power during rest. Next, deep learning methods were shown to increase the BCI classification accuracy and highlight the merit of EEG with full scalp coverage. In conclusion, we were able to demonstrate BCI performance can be improved through both behavioral and computational methods, which may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.enalpha rhythmsBCIdeep learningmeditationThesis or Dissertation