Woradorn Wattanapanitch

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Rahul Sarpeshkar16
John P. Donoghue12
Mijail D Serruya10
Jon A Mukand8
Richard D Penn8
9Rahul Sarpeshkar
7Vikram Chaturvedi
7Bharadwaj S. Amrutur
7Benjamin I. Rapoport
6Michale S. Fee

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An Energy-Efficient Micropower Neural Recording Amplifier
This paper describes an ultralow-power neural recording amplifier. The amplifier appears to be the lowest power and most energy-efficient neural recording amplifier reported to date. We describe low-noise design techniques that help the neural amplifier achieve input-referred noise that is near the theoretical limit of any amplifier using a differential… (More)
Low-Power Circuits for Brain-Machine Interfaces
This paper presents work on ultra-low-power circuits for brain-machine interfaces with applications for paralysis prosthetics, stroke, Parkinson's disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog… (More)
A Low-Power 32-Channel Digitally Programmable Neural Recording Integrated Circuit
We report the design of an ultra-low-power 32-channel neural-recording integrated circuit (chip) in a 0.18 μ m CMOS technology. The chip consists of eight neural recording modules where each module contains four neural amplifiers, an analog multiplexer, an A/D converter, and a serial programming interface. Each amplifier can be programmed to record either… (More)
Efficient Universal Computing Architectures for Decoding Neural Activity
The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs). Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they… (More)
A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders.
Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in… (More)
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