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
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;
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
DOI :
10.1109/GreenCom-iThings-CPSCom.2013.178