A 16-channel neural recording system-on-chip with CHT feature extraction processor in 65-nm CMOS

Uran, Arda and Ture, Kerim and Aprile, Cosimo and Trouillet, Alix and Fallegger, Florian and Revol, Emilie C. M. and Emami, Azita and Lacour, Stephanie P. and Dehollain, Catherine and Leblebici, Yusuf and Cevher, Volkan (2022) A 16-channel neural recording system-on-chip with CHT feature extraction processor in 65-nm CMOS. IEEE Journal of Solid-State Circuits, 57 (9). pp. 2752-2763. ISSN 0018-9200 (Print) 1558-173X (Online)

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Next-generation invasive neural interfaces require fully implantable wireless systems that can record from a large number of channels simultaneously. However, transferring the recorded data from the implant to an external receiver emerges as a significant challenge due to the high throughput. To address this challenge, this article presents a neural recording system-on-chip that achieves high resource and wireless bandwidth efficiency by employing on-chip feature extraction. Energy-area-efficient 10-bit 20-kS/s front end amplifies and digitizes the neural signals within the local field potential (LFP) and action potential (AP) bands. The raw data from each channel are decomposed into spectral features using a compressed Hadamard transform (CHT) processor. The selection of the features to be computed is tailored through a machine learning algorithm such that the overall data rate is reduced by 80% without compromising classification performance. Moreover, the CHT feature extractor allows waveform reconstruction on the receiver side for monitoring or additional post-processing. The proposed approach was validated through in vivo and off-line experiments. The prototype fabricated in 65-nm CMOS also includes wireless power and data receiver blocks to demonstrate the energy and area efficiency of the complete system. The overall signal chain consumes 2.6 μW and occupies 0.021 mm2 per channel, pointing toward its feasibility for 1000-channel single-die neural recording systems.
Item Type: Article
Uncontrolled Keywords: Compressed Hadamard transform (CHT); implantable system-on-chip (SoC); machine learning (ML); neural recording; resource efficiency; seizure detection; spreading depolarization (SD); wireless power and data transfer (WPDT)
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
President's Office
Depositing User: Yusuf Leblebici
Date Deposited: 21 Mar 2023 15:38
Last Modified: 21 Mar 2023 15:38
URI: https://research.sabanciuniv.edu/id/eprint/45075

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