Development of a Practical Visual-Evoked Potential-Based Brain-Computer Interface

There are many different neuromuscular disorders that disrupt the normal communication pathways between the brain and the rest of the body. These diseases often leave patients in a `locked-in state, rendering them unable to communicate with their environment despite having cognitively normal brain function. Brain-computer interfaces (BCIs) are augmentative communication devices that establish a direct link between the brain and a computer. Visual evoked potential (VEP)- based BCIs, which are dependent upon the use of salient visual stimuli, are amongst the fastest BCIs available and provide the highest communication rates compared to other BCI modalities. However. the majority of research focuses solely on improving the raw BCI performance; thus, most visual BCIs still suffer from a myriad of practical issues that make them impractical for everyday use. The focus of this dissertation is on the development of novel advancements and solutions that increase the practicality of VEP-based BCIs. The presented work shows the results of several studies that relate to characterizing and optimizing visual stimuli. improving ergonomic design. reducing visual irritation, and implementing a practical VEP-based BCI using an extensible software framework and mobile devices platforms

Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time

This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and interventions. We implement two components of the Cycle-of-Learning for Autonomous Systems, which is our framework for combining multiple modalities of human interaction. The current effort employs human demonstrations to teach a desired behavior via imitation learning, then leverages intervention data to correct for undesired behaviors produced by the imitation learner to teach novel tasks to an autonomous agent safely, after only minutes of training. We demonstrate this method in an autonomous perching task using a quadrotor with continuous roll, pitch, yaw, and throttle commands and imagery captured from a downward-facing camera in a high-fidelity simulated environment. Our method improves task completion performance for the same amount of human interaction when compared to learning from demonstrations alone, while also requiring on average 32% less data to achieve that performance. This provides evidence that combining multiple modes of human interaction can increase both the training speed and overall performance of policies for autonomous systems.Comment: 9 pages, 6 figure