Automated Detection of Evoked Potentials Produced by Intracranial Electrical Stimulation

Abstract

Neural responses to pulses of electrical stimulation, termed “evoked potentials”, can map brain connectivity and optimize deep brain stimulation as used in the treatment of neurological disease. As human neurotechnology now allows for simultaneous real-time sensing and stimulation at multiple channels throughout the brain, it will benefit from automated real-time detection of evoked potentials to prospectively guide brain stimulation targeting. Here we used intracranial brain stimulation data collected from 22 epilepsy patients undergoing seizure monitoring to design and evaluate an automated strategy for detecting evoked potentials produced by electrical brain stimulation. We evaluate and demonstrate the utility of two features - a high-frequency broadband power ratio, and cross-correlation across repeated stimulation trials - in detecting evoked potentials, showing that cross-correlation is a robust feature that can achieve 93% detection accuracy alone. We also show that combining these complementary features into a single metric improves detection performance over single features, and we present a complementary strategy for stimulation artifact rejection that improves detection performance of all features. In conclusion, we present an automated strategy for detecting evoked potentials that can be applied to large-scale brain data and used online to optimize brain stimulation targeting in applications such as Parkinson's disease, epilepsy, and more.