A 1.5 μW NEO-based spike detector with adaptive-threshold for calibration-free multichannel neural interfaces
A 1.5 μW NEO-based spike detector with adaptive-threshold for calibration-free multichannel neural interfaces
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DOI：
10.1109/iscas.2013.6572243
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发表时间：
2013-05
期刊：
影响因子：
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通讯作者：
Ermis Koutsos;Sivylla E. Paraskevopoulou;T. Constandinou
中科院分区：
文献类型：
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作者：
Ermis Koutsos;Sivylla E. Paraskevopoulou;T. Constandinou
本文提出了一种新的前端电路，用于通过实时，自适应算法来确定鲁棒检测尖峰的有效阈值，以检测细胞外神经记录中的动作电位。首先，通过使用非线性能量操作员（NEO）来预处理输入信号，从而有效地提高了峰值检测的尖峰特征的信噪比（SNR）然后，通过跟踪峰值NEO响应并应用非线性增益来确定阈值，以实现对不同的尖峰放大器和背景噪声水平的自适应响应。错误地检测到的尖峰和/或遇到的尖峰已在市售的0.18μm技术中实现1.8 V的供应和仅为0.03 mm2硅面积的紧凑型足迹。
This paper presents a novel front-end circuit for detecting action potentials in extracellular neural recordings. By implementing a real-time, adaptive algorithm to determine an effective threshold for robustly detecting a spike, the need for calibration and/or external monitoring is eliminated. The input signal is first pre-processed by utilising a non-linear energy operator (NEO) to effectively boost the signal-to-noise ratio (SNR) of the spike feature of interest. The spike detection threshold is then determined by tracking the peak NEO response and applying a non-linear gain to realise an adaptive response to different spike amplitudes and background noise levels. The proposed algorithm and its implementation is shown to achieve both accurate and robust spike detection, by minimising falsely detected spikes and/or missed spikes. The system has been implemented in a commercially available 0.18μm technology requiring a total power consumption of 1.5μW from a 1.8 V supply and occupying a compact footprint of only 0.03 mm2 silicon area. The proposed circuit is thus ideally suited for highchannel count, calibration-free, neural interfaces.