Highly Accurate Dual-Band Cellular Field Potential Acquisition For Brain&#x2013;Machine Interface ABSTRACT:Cellular field potential includes local field potential (LFP, 0.1 Hz–200 Hz) and spike potential (SP, 200 Hz–10 kHz). In physiological studies of the brain, SP signal has been the focus. Various circuits have been reported to acquire SP signals in brain–machine interface (BMI) systems over the years. Recent study shows that the LFP signal plays important roles in modulating many profound neuronal mechanisms in the brain. It is important for new BMI design to record the dual-band signal accurately, which demands acquisition circuits to have low noise and good linearity in both bands. In this paper, we report the design of a dual-band acquisition integrated circuit (IC) for microelectrode recording. The novel design uses a continuous- time (CT) front-end with chopping to suppress the noise, and a discrete-time (DT) back-end to achieve good linearity. A prototype monolithic acquisition IC is fabricated in a 0.35 $mu{rm m}$ CMOS process. It has 16 acquisition channels and an 11 bit successive-approximation (SAR) analog-to-digital converter (ADC). Silicon measurements show that every channel has $29.2,{rm nV}/{rm Hz}^{0.5}$ noise and ${<}0.1%$ nonlinearity. The good linearity effectively prevents the aliasing and mixing between the two bands. For LFP signals, the recording noise is $0.9,{mu}{rm V}_{rm rms}$. For SP signals, the recording noise is $2.9,{mu}{rm V}_{rm rms}$. Important to the microelectrode recording, the new design has high input impedance $(320~{rm M}Omega$ at $1,{rm kH-})$, high common-mode rejection ratio (CMRR) $({> 110}~{rm dB})$ and power-supply rejection ratio (PSRR) $({> 110}~{rm dB})$. Noise-efficiency factor (NEF) of the acquisition channel is 6.6. The IC is experimented with rat cardio-myocytes recording. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Feature Selection for Automatic Burst Detection in Neonatal Electroencephalogram High-Performance and Scalable System Architecture for the Real-Time Estimation of Generalized Laguerre-Volterra MIMO Model From Neural Population Spiking Activity