IEEE Trans Biomed Circuits Syst. 2008 Sep;2(3):173-83. doi: 10.1109/TBCAS.2008.2003198.
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 systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented.
本文介绍了应用于瘫痪假肢、中风、帕金森病、癫痫、盲人假肢和实验神经科学系统的脑机接口超低功耗电路的工作。这些电路包括具有自适应功率偏置的微功耗神经放大器，用于多电极阵列；用于数据压缩的模拟线性解码和学习架构；用于数据遥测的低功耗射频 (RF) 阻抗调制电路，可最大限度地降低体内植入系统的功耗；高效的功率传输无线链路；用于提高效率、鲁棒性和可编程性的混合信号系统集成；以及具有节能睡眠模式和唤醒模式的神经元无线刺激电路。本文介绍了从斑马雀脑中刺激和记录神经元的芯片的实验结果，以及 RF 功率链路、RF 数据链路、电极记录和电极刺激系统的结果。还介绍了模拟学习电路的仿真结果，该电路成功解码了来自猴子大脑的预先录制的神经信号。
