Department of Electrical Engineering, University of California, Los Angeles, CA 90095, USA.
IEEE Trans Neural Syst Rehabil Eng. 2010 Oct;18(5):469-78. doi: 10.1109/TNSRE.2010.2051683. Epub 2010 Jun 3.
Applications such as brain-machine interfaces require hardware spike sorting in order to 1) obtain single-unit activity and 2) perform data reduction for wireless data transmission. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection, feature-extraction, and dimensionality-reduction algorithms for spike sorting are described and evaluated in terms of accuracy versus complexity. The nonlinear energy operator is chosen as the optimal spike-detection algorithm, being most robust over noise and relatively simple. Discrete derivatives is chosen as the optimal feature-extraction method, maintaining high accuracy across signal-to-noise ratios with a complexity orders of magnitude less than that of traditional methods such as principal-component analysis. We introduce the maximum-difference algorithm, which is shown to be the best dimensionality-reduction method for hardware spike sorting.
应用，如脑机接口，需要硬件尖峰排序，以便 1）获得单单元活动和 2）进行数据减少为无线数据传输。这样的系统必须是低功率，低面积，高精度，自动，并能够实时操作。几种检测，特征提取，和降维算法的尖峰排序在精度与复杂度方面进行了描述和评估。非线性能量算子被选为最优的尖峰检测算法，在噪声方面最为稳健，而且相对简单。离散导数被选为最优的特征提取方法，在信噪比高的情况下保持高精度，其复杂度比传统方法，如主成分分析，低几个数量级。我们引入最大差算法，它被证明是硬件尖峰排序的最佳降维方法。
