TY  - INPR
Y1  - 2022/03/31/
A1  - Uran, Arda
A1  - Ture, Kerim
A1  - Aprile, Cosimo
A1  - Trouillet, Alix
A1  - Fallegger, Florian
A1  - Revol, Emilie CM
A1  - Emami, Azita
A1  - Lacour, Stephanie P
A1  - Dehollain, Catherine
A1  - Leblebici, Yusuf
A1  - Cevher, Volkan
UR  - https://doi.org/10.1109/JSSC.2022.3161296
EP  - 12
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
N2  - 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 ?W and occupies 0.021 mm² per channel, pointing toward its feasibility for 1000-channel single-die neural recording systems.
ID  - discovery10147140
AV  - public
KW  - Compressed Hadamard transform (CHT)
KW  -  implantable system-on-chip (SoC)
KW  -  machine learning (ML)
KW  -  neural recording
KW  -  resource efficiency
KW  -  seizure detection
KW  -  spreading depolarization (SD)
KW  -  wireless power and data transfer (WPDT)
PB  - Institute of Electrical and Electronics Engineers
TI  - A 16-Channel Neural Recording System-on-Chip With CHT Feature Extraction Processor in 65-nm CMOS
JF  - IEEE Journal of Solid-State Circuits
ER  -