A 32-Channel Time-Multiplexed Artifact-Aware Neural Recording System

Autor: Manuel Delgado-Restituto, Norberto Perez-Prieto, Manuel Alvarez-Dolado, Ángel Rodríguez-Vázquez
Přispěvatelé: Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Digital.CSIC. Repositorio Institucional del CSIC
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Popis: This paper presents a low-power, low-noise microsystem for the recording of neural local field potentials or intracranial electroencephalographic signals. It features 32 timemultiplexed channels at the electrode interface and offers the possibility to spatially delta encode data to take advantage of the large correlation of signals captured from nearby channels. The circuit also implements a mixed-signal voltage-triggered autoranging algorithm which allows to attenuate large interferers in digital domain while preserving neural information. This effectively increases the system dynamic range and avoids the onset of saturation. A prototype, fabricated in a standard 180nm CMOS process, has been experimentally verified in-vitro with cellular cultures of primary cortical neurons from mice. The system shows an integrated input-referred noise in the 0.5–200 Hz band of 1.4 Vrms for a spot noise of about 85 nV= p Hz. The system draws 1.5 W per channel from 1.2V supply and obtains 71 dB + 26 dB dynamic range when the artifact-aware autoranging mechanism is enabled, without penalising other critical specifications such as crosstalk between channels or commonmode and power supply rejection ratios.
Databáze: OpenAIRE