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 linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode-recording and electrode-stimulating ...

• Type: Conference
• Year: 2007
• Where: ISCAS
• Authors: Rahul Sarpeshkar, Woradorn Wattanapanitch, Benjamin I. Rapoport, Scott K. Arfin, Michael W. Baker, Soumyajit Mandal, Michale S. Fee, Sam Musallam, Richard A. Andersen
• Keywords: Adaptive Power Biasing, Analog Learning Circuits, ISCAS 2007, Ultra-low-power Circuits