A low-power integrated circuit for analog spike detection and sorting in neural prosthesis systems

A. Bonfanti, T. Borghi, R. Gusmeroli, G. Zambra, A. Spinelli, Andriy Oliynyk, L. Fadiga, G. Baranauskas
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引用次数: 21

Abstract

Since the proof of viability of prosthetic devices directly controlled by neurons, there is a huge increase in the interest on integrated multichannel recording systems to register neural signals with implanted chronic electrodes. One of the bottlenecks in such compact systems is the limited rate of data transmission by the wireless link, requiring some sort of data compression/reduction. We propose an analog low power integrated system for action potential (AP) detection and sorting that reduces the output data rate ~100 times. In this system, AP detection is performed by a double threshold method that reduces the probability of false detections while AP sorting is based on the measurement of peak and trough amplitudes and peak width. The circuit has been implemented in 0.35 - mum CMOS technology with power consumption of 70 muW per channel including the pre-amplifier. The system was tested with real recorded traces: compared to standard AP sorting techniques, the proposed simple AP sorter was able to correctly assign to single units over 90% of detected APs.
神经假体系统中模拟尖峰检测与分类的低功耗集成电路
由于证明了由神经元直接控制的假肢装置的可行性,人们对集成多通道记录系统的兴趣大大增加,该系统可以用植入的慢性电极记录神经信号。这种紧凑系统的瓶颈之一是无线链路的数据传输速率有限,需要某种形式的数据压缩/缩减。我们提出了一种模拟低功耗集成系统,用于动作电位(AP)检测和分类,可将输出数据速率降低约100倍。在该系统中,AP检测采用双阈值方法,降低了误检的概率,而AP排序则基于峰谷幅度和峰宽的测量。该电路采用0.35 μ m CMOS技术实现,包括前置放大器在内的每通道功耗为70 μ w。用真实记录的轨迹对系统进行了测试:与标准AP分选技术相比,所提出的简单AP分选器能够正确地将超过90%的检测到的AP分配到单个单元。
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