Robust, Adaptive Stimulus Artifact Cancellation Enabling Fully Bidirectional Neural Interfaces

Abstract

This work explores the design and implementation of a neural stimulus artifact cancellation system that enables reconfigurable bidirectional interfaces by allowing simultaneous electrical stimulation and electrical recording in the same region of neural tissue. This system is designed to be compatible with any stimulator or recording front end, and its efficacy is demonstrated and challenged by implementation with an H-Bridge stimulator and highly multiplexed mixed-mode feedback recording front-end. The canceller topology is adaptive, learning the stimulus artifact and tracking changes in stimulation or neural tissue characteristics. The adaptation algorithm and canceller topology is highly scalable to any length of stimulation artifact or number of simultaneous recording channels. The canceller has been implemented in CMOS and further prototyped on an FPGA. Real-time system validation has been performed with separate recording and stimulator chips, demonstrating compatibility with various front-end topologies. Voltage suppression capabilities are demonstrated up to 44dB, with signal-to-noise ratio improvements of up to 66dB possible by bringing the recording channel back into its linear range.