A low-power low-data-rate neural recording system with adaptive spike detection

A design of small, low-power, low-data rate, wireless 32-channel neural recording system for small animal head-stage is presented. A neural pre-amplifier has low-input-referred-noise of 1.95 muVrms and consumes 53.6 muW. To enable digital telemetry with optimized bandwidth under size and power constraint for small-animal headstage, we propose to separately record spikes and local-field potentials. An adaptive spike detector using absolute value algorithm accompanied with 7th-order all-pass delay filter provides accurate on-chip acquisition of spike waveform in duration of 2 ms. A low-power 10-bit and 5-bit resolution A/D converters running at 22 Ksamples/s for active spikes and 200 samples/s for local field potential, respectively, can be integrated with the proposed system. Using adaptive bandwidth control, we achieve reduction of data-rate up to seven times which provides compatibility to 1 Mbps ultra low power Bluetooth technology. Total power consumption of single channel excluding ADCs is 109.58 muW in 3.3 V power supply

Low-Power High-Resolution 32-channel Neural Recording System

A design of low-power 32-channel neural recording system with on-chip high-resolution A/D converters is presented. A neural front-end including low-noise fully differential pre-amplifier, gain stage, and buffer consumes only 56 muW. Two 13-bits extended counting A/D converters running at 512 KHz sampling rate are integrated with 32 neural front-ends on a chip. The experimental prototype was designed in 0.6 mum CMOS process. With a 3.3 V power supply, total power consumption of a chip is 22 mW and the whole system occupies an area of 3 mm times 3 mm

test results for low power bearing estimator sensor nodes

Abstract-This paper describes experimental results of low power sensor nodes designed to perform bearing estimation. The nodes are intended to form a wireless sensor network able to locate an audio source. Two different nodes are tested: one is based on a Cross-correlation Derivative integrated circuit (IC), and the other on a Gradient Flow IC. Implementation details and experimental results of both systems working in a natural environment are presented

A low-noise differential microphone inspired by the ears of the parasitoid fly Ormia ochracea

A miniature differential microphone is described having a low-noise floor. The sensitivity of a differential microphone suffers as the distance between the two pressure sensing locations decreases, resulting in an increase in the input sound pressure-referred noise floor. In the microphone described here, both the diaphragm thermal noise and the electronic noise are minimized by a combination of novel diaphragm design and the use of low-noise optical sensing that has been integrated into the microphone package. The differential microphone diaphragm measures 1×2 mm2 and is fabricated out of polycrystalline silicon. The diaphragm design is based on the coupled directionally sensitive ears of the fly Ormia ochracea. The sound pressure input-referred noise floor of this miniature differential microphone has been measured to be less than 36 dBA