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An Energy-Efficient Micropower Neural Recording Amplifier

• Woradorn Wattanapanitch, Michale S. Fee, Rahul Sarpeshkar
• IEEE Trans. Biomed. Circuits and Systems
• 2007

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

• Rahul Sarpeshkar, Woradorn Wattanapanitch, +6 authors Richard A. Andersen
• IEEE Trans. Biomed. Circuits and Systems
• 2007

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

• Woradorn Wattanapanitch, Rahul Sarpeshkar
• IEEE Trans. Biomed. Circuits and Systems
• 2011

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

• Benjamin I. Rapoport, Lorenzo Turicchia, Woradorn Wattanapanitch, Thomas J. Davidson, Rahul Sarpeshkar
• PloS one
• 2012

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.

• Benjamin I Rapoport, Woradorn Wattanapanitch, Hector L Penagos, Sam Musallam, Richard A Andersen, Rahul Sarpeshkar
• Conference proceedings : ... Annual International…
• 2009

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)

A 10 Gb/s optical receiver in 0.25 µm silicon-on-sapphire CMOS

• Paul C. P. Chen, Anand Pappu, Zhongtao Fu, Woradorn Wattanapanitch, Alyssa B. Apsel
• ISCAS
• 2008

Analog VLSI and Biological Systems

• Rahul Sarpeshkar, Lorenzo Turicchia, +13 authors Gretchen Jones
• 2006

Introduction Our group's research focuses on BIOELECTRONICS: We work in 3 interdisciplinary areas, biomedical electronics, bio-inspired electronics, and circuit modeling of biology. Our work advances frontiers and has applications in ultra-low-power, analog, RF (wireless), micro-sensing (mechanical, optical, fluidic, chemical), ultra-low-noise,… (More)

An ultra low power implantable neural recording system for brain-machine interfaces

• Woradorn Wattanapanitch
• 2011

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