Characterization of Language Cortex Activity During Speech Production and Perception

Millions of people around the world suffer from severe neuromuscular disorders such as spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis (ALS), and others. Many of these individuals cannot perform daily tasks without assistance and depend on caregivers, which adversely impacts their quality of life. A Brain-Computer Interface (BCI) is technology that aims to give these people the ability to interact with their environment and communicate with the outside world. Many recent studies have attempted to decode spoken and imagined speech directly from brain signals toward the development of a natural-speech BCI. However, the current progress has not reached practical application. An approach to improve the performance of this technology is to better understand the underlying speech processes in the brain for further optimization of existing models. In order to extend research in this direction, this thesis aims to characterize and decode the auditory and articulatory features from the motor cortex using the electrocorticogram (ECoG). Consonants were chosen as auditory representations, and both places of articulation and manners of articulation were chosen as articulatory representations. The auditory and articulatory representations were decoded at different time lags with respect to the speech onset to determine optimal temporal decoding parameters. In addition, this work explores the role of the temporal lobe during speech production directly from ECoG signals. A novel decoding model using temporal lobe activity was developed to predict a spectral representation of the speech envelope during speech production. This new knowledge may be used to enhance existing speech-based BCI systems, which will offer a more natural communication modality. In addition, the work contributes to the field of speech neurophysiology by providing a better understanding of speech processes in the brain

Network Modeling of Motor Pathways from Neural Recordings

During cued motor tasks, for both speech and limb movement, information propagates from primary sensory areas, to association areas, to primary and supplementary motor and language areas. Through the recent advent of high density recordings at multiple scales, it has become possible to simultaneously observe activity occurring from these disparate regions at varying resolution. Models of brain activity generally used in brain-computer interface (BCI) control do not take into account the global differences in recording site function, or the interactions between them. Through the use of connectivity measures, however, it has been made possible to determine the contribution of individual recording sites to the global activity, as they vary with task progression. This dissertation extends those connectivity models to provide summary information about the importance of individual sites. This is achieved through the application of network measures on the adjacency structure determined by connectivity measures. Similarly, by analyzing the coordinated activity of all of the electrode sites simultaneously during task performance, it is possible to elucidate discrete functional units through clustering analysis of the electrode recordings. In this dissertation, I first describe a BCI system using simple motor movement imagination at single recording sites. I then incorporate connectivity through the use of TV-DBN modeling on higher resolution electrode recordings, specifically electrocorticography (ECoG). I show that PageRank centrality reveals information about task progression and regional specificity which was obscured by direct application of the connectivity measures, due to the combinatorial increase in feature dimensionality. I then show that clustering of ECoG recordings using a method to determine the inherent cluster count algorithmically provides insight into how network involvement in task execution evolves, though in a manner dependent on grid coverage. Finally, I extend clustering analysis to show how individual neurons in motor cortex form distinct functional communities. These communities are shown to be task-specific, suggesting that neurons can form functional units with distinct neural populations across multiple recording sites in a context dependent impermanent manner. This work demonstrates that network measures of connectivity models of neurophysiological recordings are a rich source of information relevant to the field of neuroscience, as well as offering the promise of improved degree-of-freedom and naturalness possible through direct BCI control. These models are shown to be useful at multiple recording scales, from cortical-area level ECoG, to highly localized single unit microelectrode recordings