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A 16-Channel Wireless Neural Recording System-on-Chip with CHT Feature Extraction Processor in 65nm CMOS

Uran, A; Ture, K; Aprile, C; Trouillet, A; Fallegger, F; Emami, A; Lacour, SP; ... Cevher, V; + view all (2021) A 16-Channel Wireless Neural Recording System-on-Chip with CHT Feature Extraction Processor in 65nm CMOS. In: Proceedings of the 2021 IEEE Custom Integrated Circuits Conference (CICC). IEEE: Austin, TX, USA. Green open access

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Abstract

Wireless implantable neural recording chips enable multichannel data acquisition with high spatiotemporal resolution in situ. Recently, the use of machine learning approaches on neural data for diagnosis and prosthesis control have renewed the interest in this field, and increased even more the demand for multichannel data. However, simultaneous data acquisition from many channels is a grand challenge due to data rate and power limitations on wireless transmission for implants. As a result, recent studies have focused on on-chip classifiers, despite the fact that only primitive classifiers can be placed on resource-constrained chips. Moreover, robustness of the chosen algorithm cannot be guaranteed pre-implantation due to the scarcity of patient-specific data; waveforms can change over time due to electrode micro migration or tissue reaction, highlighting the need for robust adaptive features.

Type: Proceedings paper
Title: A 16-Channel Wireless Neural Recording System-on-Chip with CHT Feature Extraction Processor in 65nm CMOS
Event: 2021 IEEE Custom Integrated Circuits Conference (CICC)
ISBN-13: 978-1-7281-7581-2
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/CICC51472.2021.9431458
Publisher version: https://doi.org/10.1109/CICC51472.2021.9431458
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Wireless communication, Electrodes, Data acquisition, Machine learning, Implants, Feature extraction, Robustness
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10136240
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