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Browsing by Subject "BCI"

Now showing 1 - 2 of 2
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    How to Use Brains and Computers to Enhance Brain Computer Interfacing
    (2020-11) Stieger, James
    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.
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    Targeting the Brain in Brain-Computer Interfacing: The Effect of Transcranial Current Stimulation and Control of a Physical Effector on Performance and Electrophysiology Underlying Noninvasive Brain-Computer Interfaces
    (2017-07) Baxter, Bryan
    Brain-computer interfaces (BCIs) and neuromodulation technologies have recently begun to fulfill their promises of restoring function, improving rehabilitation, and enhancing abilities and learning. However, lengthy user training to achieve acceptable accuracy is a barrier to BCI acceptance and use by patients and the general population. Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation technology whereby a low level of electrical current is injected into the brain to alter neural activity and has been found to improve motor learning and task performance. A barrier to optimizing behavioral effects of tDCS is that we do not yet understand how neural networks are affected by stimulation and how stimulation interacts with ongoing endogenous activity. The purpose of this dissertation was to elucidate strategies to improve BCI control by targeting the user through two approaches: 1. Subject control of a robotic arm to enhance user motivation and 2. tDCS application to improve behavioral outcomes and alter networks underlying sensorimotor rhythm-based BCI performance. The primary results illustrate that targeted tDCS of the motor network interacts with task specific neural activity to improve BCI performance and alter neural electrophysiology. This effect on neural activity extended across the task network, beyond the area of direct stimulation, and altered connectivity unilaterally and bilaterally between frontal and parietal cortical regions. These findings suggest targeted neuromodulation interacts with endogenous neural activity and can be used to improve motor-cognitive task performance.