Leveraging Spatiotemporal Relationships of High-frequency Activation in Human Electrocorticographic Recordings for Speech Brain-Computer-Interface

Speech production is one of the most intricate yet natural human behaviors and is most keenly appreciated when it becomes difficult or impossible; as is the case for patients suffering from locked-in syndrome. Burgeoning understanding of the various cortical representations of language has brought into question the viability of a speech neuroprosthesis using implanted electrodes. The temporal resolution of intracranial electrophysiological recordings, frequently billed as a great asset of electrocorticography (ECoG), has actually been a hindrance as speech decoders have struggled to take advantage of this timing information. There have been few demonstrations of how well a speech neuroprosthesis will realistically generalize across contexts when constructed using causal feature extraction and language models that can be applied and adapted in real-time. The research detailed in this dissertation aims primarily to characterize the spatiotemporal relationships of high frequency activity across ECoG arrays during word production. Once identified, these relationships map to motor and semantic representations of speech through the use of algorithms and classifiers that rapidly quantify these relationships in single-trials. The primary hypothesis put forward by this dissertation is that the onset, duration and temporal profile of high frequency activity in ECoG recordings is a useful feature for speech decoding. These features have rarely been used in state-of-the-art speech decoders, which tend to produce output from instantaneous high frequency power across cortical sites, or rely upon precise behavioral time-locking to take advantage of high frequency activity at several time-points relative to behavioral onset times. This hypothesis was examined in three separate studies. First, software was created that rapidly characterizes spatiotemporal relationships of neural features. Second, semantic representations of speech were examined using these spatiotemporal features. Finally, utterances were discriminated in single-trials with low latency and high accuracy using spatiotemporal matched filters in a neural keyword-spotting paradigm. Outcomes from this dissertation inform implant placement for a human speech prosthesis and provide the scientific and methodological basis to motivate further research of an implant specifically for speech-based brain-computer-interfaces

From First Contact to Close Encounters: A Developmentally Deep Perceptual System for a Humanoid Robot

This thesis presents a perceptual system for a humanoid robot that integrates abilities such as object localization and recognition with the deeper developmental machinery required to forge those competences out of raw physical experiences. It shows that a robotic platform can build up and maintain a system for object localization, segmentation, and recognition, starting from very little. What the robot starts with is a direct solution to achieving figure/ground separation: it simply 'pokes around' in a region of visual ambiguity and watches what happens. If the arm passes through an area, that area is recognized as free space. If the arm collides with an object, causing it to move, the robot can use that motion to segment the object from the background. Once the robot can acquire reliable segmented views of objects, it learns from them, and from then on recognizes and segments those objects without further contact. Both low-level and high-level visual features can also be learned in this way, and examples are presented for both: orientation detection and affordance recognition, respectively. The motivation for this work is simple. Training on large corpora of annotated real-world data has proven crucial for creating robust solutions to perceptual problems such as speech recognition and face detection. But the powerful tools used during training of such systems are typically stripped away at deployment. Ideally they should remain, particularly for unstable tasks such as object detection, where the set of objects needed in a task tomorrow might be different from the set of objects needed today. The key limiting factor is access to training data, but as this thesis shows, that need not be a problem on a robotic platform that can actively probe its environment, and carry out experiments to resolve ambiguity. This work is an instance of a general approach to learning a new perceptual judgment: find special situations in which the perceptual judgment is easy and study these situations to find correlated features that can be observed more generally

Department of Defense Dictionary of Military and Associated Terms

The Joint Publication 1-02, Department of Defense Dictionary of Military and Associated Terms sets forth standard US military and associated terminology to encompass the joint activity of the Armed Forces of the United States. These military and associated terms, together with their definitions, constitute approved Department of Defense (DOD) terminology for general use by all DOD components

YOUMARES 9 - The Oceans: Our Research, Our Future

This open access book summarizes peer-reviewed articles and the abstracts of oral and poster presentations given during the YOUMARES 9 conference which took place in Oldenburg, Germany, in September 2018. The aims of this book are to summarize state-of-the-art knowledge in marine sciences and to inspire scientists of all career stages in the development of further research. These conferences are organized by and for young marine researchers. Qualified early-career researchers, who moderated topical sessions during the conference, contributed literature reviews on specific topics within their research field