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