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A 330μW, 64-channel neural recording sensor with embedded spike feature extraction and auto-calibration

 

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Opened Access A 330μW, 64-channel neural recording sensor with embedded spike feature extraction and auto-calibration
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Author: Rodríguez Pérez, Alberto
Delgado Restituto, Manuel
Darie, Ángela
Soto Sánchez, Cristina
Fernández Jover, Eduardo
Rodríguez Vázquez, Ángel Benito
Department: Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo
Date: 2014
Published in: IEEE 2014 Asian Solid-State Circuits Conference: 205-208
ISBN/ISSN: 978-1-4799-4090-5
Document type: Presentation
Abstract: his paper reports an integrated 64-channel neural recording sensor. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration mechanism which configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by an embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330μW.
Size: 2.030Mb
Format: PDF

URI: http://hdl.handle.net/11441/33648

DOI: http://dx.doi.org/10.1109/ASSCC.2014.7008896

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