Paper Info
Title
26.9 A 0.19×0.17mm2 Wireless Neural Recording IC for Motor Prediction with Near-Infrared-Based Power and Data Telemetry.
Abstract
Brain machine interfaces using neural recording systems [1]–[4] can enable motor prediction [5]–[6] for accurate arm and hand control in paralyzed or severely injured individuals. However, implantable systems have historically used wires for data communication and power, increasing risks of tissue damage, infection, and cerebrospinal fluid leakage, rendering these devices unsuitable for long-term implantation (Fig. 26.9.1, top). Recently, several wireless and miniaturized neural recording implants with various power and data transmission methods were proposed. References [7], [8] propose an electrocorticography (ECoG) recording system with near-field RF power transfer and bilateral communication, but the 0.5W Tx exceeds maximum exposure limits by 10x [8]. Ultrasonic telemetry can safely send more power than RF; however it requires mm-scale dimensions (0.8mm3 in [9]) due to bulky ultrasound transducers. On the other hand, near infrared (NIR) light can provide power transfer and data downlink via a photovoltaic cell (PV), and a data uplink via a light-emitting diode (LED). Dimensions can be scaled to 100s of microns [10], with [11] demonstrating a 0.0297mm2 neural recording system using a 50mW/mm2 light source $( of safety limit for the brain). However, this system is limited to a single channel, and since it only has a surface electrode, it can record only surface potentials (face-down, potentially blocking the light channel) or must itself be injected into brain tissue, creating significant tissue damage and danger of bleeding. In this paper, we propose $0.74\\mu \\mathrm{W}, 0.19\\times 0.17\\mathrm{mm}^{2}1\\mathrm{C}$ designed for a wireless neural recording probe. It computes so-called spiking band power (SBP) [5], [12] on-chip to save 920x power while maintaining accurate finger position and velocity decoding.
Year
DOI
Venue
2020
10.1109/ISSCC19947.2020.9063005
ISSCC
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
14
Name
Order
Citations
PageRank
Jongyup Lim1113.69
Eunseong Moon223.46
Michael Barrow321.44
Samuel R. Nason421.77
Paras R. Patel501.35
Parag G. Patil600.34
Sechang Oh7648.47
Inhee Lee827533.89
Hun-Seok Kim9579.07
Dennis Sylvester105295535.53
David Blaauw118916823.47
Cynthia A. Chestek1200.34
J. D. Phillips13142.71
Tae-Kwang Jang1416823.34