DocumentCode
653285
Title
Ultra-low-power Neural Recording Microsystem for Implantable Brain Machine Interface
Author
Weidong Cao ; Hongge Li
Author_Institution
Sch. of Electron. Inf. Eng., Beihang Univ., Beijing, China
fYear
2013
fDate
20-23 Aug. 2013
Firstpage
1050
Lastpage
1053
Abstract
We propose an implantable CMOS micro system for detection of neural spike signals from complex brain neural potentials which achieves the characteristics of ultra low-power and high-precision. The neural recording micro system consists of a low-noise bioamplifier, a neural spike detector based on nonlinear energy operator (NEO) and a precision hysteresis comparator. The DC offset in the bioamplifier is rejected by introducing a new active feedback configuration instead of the large capacitors, the NEO algorithm is implemented through simple analog circuits operating in sub-threshold region, and the hysteresis comparator is added to determine the location of neural spike. The current consumption of the recording system is 3.02 μA with 3.3 V supply. The proposed system has been implemented in 0.35 μm CMOS process and precisely detects neural spike signals from extra cellular recording.
Keywords
CMOS analogue integrated circuits; amplifiers; brain-computer interfaces; medical signal detection; NEO; active feedback configuration; complex brain neural potentials; implantable CMOS microsystem; implantable brain machine interface; low-noise bioamplifier; neural spike signals detection; nonlinear energy operator; precision hysteresis comparator; ultra-low-power neural recording microsystem; Analog circuits; CMOS integrated circuits; Capacitors; Detectors; Electric potential; Hysteresis; Noise; high-accuracy; neural spike; nonliear energy operater; sub-threshold; ultra-low-power;
fLanguage
English
Publisher
ieee
Conference_Titel
Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing
Conference_Location
Beijing
Type
conf
DOI
10.1109/GreenCom-iThings-CPSCom.2013.178
Filename
6682192
Link To Document