Quantifying Spatio-temporal Features of Neural Activity for Human Speech Production

Abstract

Speech production is a unique human function that allows us to rapidly communicate on a regular basis. Characterizing the brain mechanisms of speech not only advances the scientific understanding of human cognition, but also provides the potential for therapeutic interventions to restore communication in patients with motor speech impairments. Deciphering this speech neural mechanisms requires measuring the functional interactions between neuro-anatomical regions at precise spatial and temporal scales, a feat that has remained difficult with standard non-invasive techniques for measuring brain activity. Intracranial electrode recordings provide measurements of neural activity directly from the surface of the brain and enable more accurate representations of speech neural activations with precise timings. Further, recent technological developments in increasing the number of electrodes have improved the spatial resolution, thereby revealing hidden neural features that would have been absent with low-resolution clinical electrodes. Therefore, developing analytical frameworks to effectively quantify these spatial and temporal characteristics of speech neural activations, can not only improve the ability to accurately decode speech from the brain, but also delineate the neural mechanism that enable speech production. This dissertation proposes signal analysis and neural decoding methods to quantify the spatiotemporal dynamics of speech neural features obtained from intracranial recordings. These computational approaches validate the use of high-resolution neural recordings to achieve successful speech decoding for neuroprosthetics and to elucidate the neural mechanisms of speech production in time and space.In the first section of this dissertation (chapter 2), high-resolution micro-electrocorticographic (µECoG) arrays are introduced as a novel recording tool to improve the accuracy of neural speech decoding. Using a comprehensive set of decoding algorithms and analytic metrics, µECoG is validated against standard intracranial recordings by demonstrating strong speech decoding with only ~2 minutes of training data. The decoding metrics were also applied to examine the spatial characteristics of µECoG. We demonstrated the importance of high channel count, high spatial coverage, high resolution, and micro-scale recording, to enable accurate speech decoding. The second section (chapter 3) extended these analytical techniques to identify neural mechanisms of speech for both planning and execution. Distinct neuro-anatomical networks that were identified were functionally specific to speech features. Further, the utilization of µECoG uncovered spatial mechanisms that facilitated the transition from speech planning to motor execution. These sets of results obtained from novel neural interfacing technologies and clinical recordings provide a robust analytical methodology to study neural mechanisms of human speech.