Estimating unmeasured invasive EEG signals using a reduced-order observer.

Epilepsy affects around 50 million people worldwide. Over 30% of patients are drug-resistant where the only treatment may be surgical resection of the epileptogenic zone (EZ), the region of the brain that generates seizures. Identification of the EZ is often based on invasive EEG recordings. As such, surgical outcome relies heavily on precise and dense placement of EEG electrodes into the brain. Despite large brain regions being removed, success rates barely reach 65%. This gives rise to the "missing electrode problem", where clinicians want to know what neural activity looks like between sparsely implanted electrodes. Solving this problem will enable more accurate localization of the EZ. In this paper, we demonstrate the first steps towards developing a computational platform to estimate neural activity at the "missing electrodes" using a reduced-order observer from control theory. Specifically, we constructed a sequence of discrete time Linear Time-Invariant (LTI) models using the available EEG data from two epilepsy patients. Then, we used the models to simulate EEG data and remove selected signals ("missing" states) from the simulated data set. Finally, we used a reduced-order observer to estimate the signals of these "missing" states and evaluated performance by comparing the observer estimates to the simulated EEG time series.