Finding Structure in Electrocorticographic Neural Signals for Brain-Machine Interface Applications
- Author(s): Omigbodun, Akinyinka Oreoluwa;
- Advisor(s): Gilja, Vikash;
- et al.
Electrocorticography (ECoG), also known as intracranial electroencephalography (iEEG), is the practice of recording electrical potentials on the cerebral cortex via electrodes placed on the exposed brain surface. ECoG has been a critical component of epilepsy medical treatment protocols involving neurosurgery for more than half a century. More recently, ECoG has emerged as a promising recording modality for brain-machine interfaces and neuroscience research. The BRAIN Initiative is representative of a renewed and concerted effort to push the boundaries of possibility in medical care and technology, and to expand our understanding of brain function. Concomitant with this new drive is a need for techniques that address the challenges posed by high-channel count ECoG signal analysis as well as by neural data collection limited due to the invasiveness of ECoG. In this dissertation, we introduce the use of a discrete-state based probabilistic method for modeling ECoG-derived signals, and contrast this method with previously existing analogous probabilistic models without a discrete component. We then explore a class of discrete-state based probabilistic models, and identify spatial and temporal model constraints that were advantageous in the analysis of a high-channel count ECoG dataset. Finally, we introduce another probabilistic model that we use for unsupervised learning of ECoG trial spatiotemporal structure, and clustering of the ECoG trials in a data-limited context.