A Machine Learning Approach for Real-time Cortical State Estimation

Abstract

ObjectiveCortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as "cortical state". Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation. ApproachWe use unsupervised Gaussian Mixture Models (GMMs) to identify discrete, emergent clusters in spontaneous local field potential (LFP) signals in cortex. We then extend our approach to a temporally-informed Hidden semi-Markov Model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments. Main resultsUnsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data. SignificanceTo our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.