A Digitally Assisted Multiplexed Neural Recording System With Dynamic Electrode Offset Cancellation via an LMS Interference-Canceling Filter

Abstract

This article presents a low-power (LP) area-efficient implantable neural recording system that supports high-density neural implant (HDNI) applications. The system uses a time-division multiple access method to record from 16-neural electrodes simultaneously. A least mean squares (LMSs) algorithm is used to cancel the slowly varying electrode offsets from all channels simultaneously by using a single-tap digital adaptive filter (AF). The presented technique is fabricated in 65-nm CMOS technology and achieves a per-channel area of 0.00248 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ; 68% of which is digital circuitry (and is thus scalable with technology). The overall system consumes 3.38 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> per channel while achieving 2.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{V}_{\mathrm {rms}}$ </tex-math></inline-formula> of input referred noise (IRN) in 10 kHz of bandwidth. The proposed system has a noise efficiency factor (NEF) of 1.83 and is fully integrated on-chip.