Low Power Analog Front-End Resilient to Large Stimulation Artifacts for Bidirectional Brain-Computer Interfaces

Abstract

Bidirectional brain-computer interfaces (BCIs) are essential for next-generation neuroprosthetics as they enable both neural stimulation and the simultaneous recording of neural activity. However, a significant challenge in these systems is the presence of stimulation artifacts: large voltage transients that saturate sensitive recording electronics and mask critical neural signals.

This thesis investigates the fundamental causes and characterization of stimulation artifacts in the context of visual prostheses through the lens of an electrode-tissue interface (ETI) model. A comprehensive review of existing artifact reduction techniques is provided, assessing their efficacy and trade-offs regarding implementation complexity and signal integrity. The review serves as a framework for the introduction of two novel artifact reduction techniques that target the residual artifact to permit recording of post-stimulation action potentials (APs).

A residual artifact reduction technique is proposed that replicates and cancels the artifact at the input of the recording analog front-end (AFE). Replication of the artifact is performed through the voltage decay of an RC network with a characteristic time constant equal to the ETI model. Although the concept of this technique is completely novel, the system implementation is impractical due to technological limitations.

A rapid charge reset technique for fast settling of artifact transients is also proposed and implemented with an AFE designed in the TSMC 40 nm CMOS technology node. The system features an AC-coupled low-noise boxcar sampler (LNBS) followed by a switched-capacitor low-pass filter (SC-LPF). The maximum artifact considered for the application of this work can be reduced from 725 mVpp to 2.1 mVpp—a reduction of approximately 50.8 dB—within a recovery time of 50 μs while maintaining a low power consumption of approximately 205 nW per channel and an input-referred integrated noise performance of 7.6 μVrms over the system bandwidth of 300 Hz - 5 kHz. Further reduction of the residual artifact requires minimization of the residual charge across the ETI that is present post-stimulation.