本論文針對棘波分類法則實現一套硬體架構，以提供大量資料快速運算。棘波排序是研究生物大腦及發展腦機介面(BMI, Brain Machine Interface)的基礎，棘波排序主要分為三個步驟：棘波偵測、特徵擷取以及分群，棘波分類則包含特徵擷取與分群部分，本論文採用OSort演算法將棘波偵測所採集到的數位訊號進行分類。
　　OSort演算法是一個模板比對（Template Matching）的非監督式分群法則，與其他棘波分類演算法相比，如PCA和K-means、小波轉換和SPC、GHA和FCM等等，不需做複雜的降低資料維度（Dimensionality Reduction）運算，更適用於即時分類；不用以分類指標（Cluster Validity Index）來計算最佳群集數，即可自動決定群集個數；不若於其他演算法需要一段時間的離線訓練（Offline Training），可立刻獲得棘波分類結果。
　　本論文保留OSort演算法核心的概念，簡化較消耗硬體資源的設計，並適用於多數的採樣數據做高速運算，透過Altera公司的系統開發工具使用NoC（Network on Chip）架構，實現並驗證於現場可程式化邏輯閘（FPGA, Field Programmable Gate Array）上，實驗結果證明，本論文所提出的棘波分類硬體架構具有一定精確度及高速運算的優點。
關鍵字： 可程式化系統晶片、棘波分類、OSort、FPGA、NoC

[1] E. R. Kandel, J. H. Schwartz and T. M. Jessell, Principles of neural science, New York: McGraw-Hill, 2000.

[2] A. L. Hodgkin and A. F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve, J. Physiol., Vol. 117, pp. 500–544, 1952.

[3] M. A. Lebedev and M. A. L. Nicolelis, Brain machine interfaces: past, present and future, Trends in Neurosciences, Vol. 29, pp. 536-546, 2006.

[4] S. Gibson, J. W. Judy and D. Markovic, Spike sorting: the first step in decoding the brain, IEEE Signal Processing Magazine, pp. 124-143, 2012.

[5] http://neurosky.com/

[6] M. S. Lewicki, A review of methods for spike sorting: The detection and classification of neural action potentials, Netw. Comput. Neural Syst., Vol. 9, pp. R53–R78, 1998.

[7] K. Pearson, On lines and planes of closest fit to systems of points in space, Phil. Mag., Vol. 2, pp. 559–572, 1901.

[8] S. J. Lin, Y. T. Hung and W. J. Hwang, Efficient hardware architecture based on generalized Hebbian algorithm for texture classification, Neurocomputing, pp. 3248-3256, 2011.

[9] R. Quiroga, Z. Nadasdy, and Y. Ben-Shaul, Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering, Neural Comput., Vol. 16, pp. 1661-1687, 2004.

[10] K. L. Wu and M. S. Yang, A cluster validity index for fuzzy clustering, Pattern Recognit. Lett., Vol. 26, pp. 1275-1291, 2005.

[11] J. Wild, Z. Prekopcsak, T. Sieger, D. Novak and R. Jech, Performance comparison of extracellular spike sorting algorithms for single-channel recordings, J. Neurosci. Methods, Vol. 203, pp. 369-376, 2012.
[12] S. Mukhopadhyay and G. C. Ray, A new interpretation of nonlinear energy operator and its efficacy in spike detection, IEEE Trans. Biomed. Eng., Vol. 45, pp. 180-187, 1998.

[13] T. Sato, T. Suzuki, and K. Mabuchi, Fast Template Matching for Spike Sorting, Electronics and Communications in Japan, Vol. 92, pp. 57-63, 2009.

[14] U. Rutishauser, Online detection and sorting of extracellularly recorded action potentials in human medial temporal lobe recordings, in vivo, J. Neurosci. Methods, Vol. 154, pp. 204-224, 2006.

[15] W. J. Hwang, S. H. Wang and Y. T. Hsu, Spike Detection Based on Normalized Correlation with Automatic Template Generation, Sensors, Vol. 14, 2014.

[16] 王思淮, 以回饋式自動模板生成為基礎之正規化關聯值棘波偵測系統之設計及實現, 2014.

[17] L.S. Smith and N. Mtetwa, A tool for synthesizing spike trains with realistic interference. J. Neurosci. Methods, Vol. 159, pp. 170-180, 2007.

[18] S. Gibson, J. W. Judy and D. Markovic, An FPGA-based platform for accelerated offline spike sorting, J. Neurosci. Methods, Vol. 215, pp. 1-11, 2013.