Litcius/Paper detail

A 2.5-20kSps in-Pixel Direct Digitization Front-End for ECoG with In-Stimulation Recording

Aditi Jain, E. Fogleman, Paul Botros, Ritwik Vatsyayan, Corentin Pochet, Andrew Bourhis, Zhaoyi Liu, S Chethan, Hanh‐Phuc Le, Ian Galton, Shadi A. Dayeh, Drew A. Hall

202410 citationsDOI

Abstract

Closed-loop neuromodulation promises to enhance treatment for movement disorders, pain, and epilepsy. Advancements in low-im-pedance, high-density recording grids [1] have paved the way for low-noise neural recording systems with high spatial and temporal resolution. However, a conventional high-density neural recording signal path with programmable gain amplifiers (PGAs) and a shared ADC [2] saturates during stimulation because of the high amplifier gain. Due to a fundamental tradeoff with the input high-pass cutoff frequency (for dc electrode offset elimination), it takes hundreds of ms to recover, leading to critical data loss. Recent advances in direct digitization-based analog front-ends (AFEs) overcome this limitation by forgoing the amplifier and directly connecting the electrode to a high dynamic range ADC. Directly using a continuous time <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Delta\Sigma$</tex> mod-ulator (CTDSM) for this application has several notable challenges: slow recovery/instability during artifacts beyond the input range, power and area limitations, and low input impedance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(Z_{\text{in}})$</tex> . We report a 4×2 array of per-pixel 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> -order <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Delta\Sigma$</tex> ADCs (including the decimation filter) for ECoG with the fastest (sub-ms) artifact recovery time, ena-bling in-stimulation recording and power-efficient bandwidth scaling.

Topics & Concepts

Computer scienceAmplifierDelta-sigma modulationPixelNoise (video)DigitizationArtificial intelligenceBandwidth (computing)TelecommunicationsImage (mathematics)Neuroscience and Neural EngineeringAdvanced Memory and Neural ComputingEEG and Brain-Computer Interfaces