2022/09/24 by Baibhab Chatterjee, Chatterjee, Baibhab, K Gaurav Kumar +9
Engineering · Neuroscience · #Advanced Memory and Neural Computing #EEG and Brain-Computer Interfaces #FOS: Biological sciences #FOS: Electrical engineering #Neurons and Cognition (q-bio.NC) #Neuroscience and Neural Engineering #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2209.11953
openalex publication_date 2022/09/24 · openalex created_date 2022/09/28 · openalex updated_date 2026/07/28
Untethered miniaturized wireless neural sensor nodes with data transmission and energy harvesting capabilities call for circuit and system-level innovations to enable ultra-low energy deep implants for brain-machine interfaces. Realizing that the energy and size constraints of a neural implant motivate highly asymmetric system design (a small, low-power sensor and transmitter at the implant, with a relatively higher power receiver at a body-worn hub), we present Time-Domain Bi-Phasic Quasi-static Brain Communication (TD- BPQBC), offloading the burden of analog to digital conversion (ADC) and digital signal processing (DSP) to the receiver. The input analog signal is converted to time-domain pulse-width modulated (PWM) waveforms, and transmitted using the recently developed BPQBC method for reducing communication power in implants. The overall SoC consumes only 1.8μW power while sensing and communicating at 800kSps. The transmitter energy efficiency is only 1.1pJ/b, which is >30X better than the state-of-the-art, enabling a fully-electrical, energy-harvested, and connected in-brain sensor/stimulator node.