Chan Hsiao-Lung, Wu Tony, Lee Shih-Tseng, Fang Shih-Chin, Chao Pei-Kuang, Lin Ming-An
Department of Electrical Engineering, Chang Gung University, Taoyuan, Taiwan.
J Neurosci Methods. 2008 Feb 15;168(1):203-11. doi: 10.1016/j.jneumeth.2007.09.017. Epub 2007 Sep 22.
Spike information is beneficial to correlate neuronal activity to various stimuli or determine target neural area for deep brain stimulation. Data clustering based on neuronal spike features provides a way to separate spikes generated from different neurons. Nevertheless, some spikes are aligned incorrectly due to spike deformation or noise interference, thereby reducing the accuracy of spike classification. In the present study, we proposed unsupervised spike classification over the reconstructed phase spaces of neuronal spikes in which the derived phase space portraits are less affected by alignment deviations. Principal component analysis was used to extract major principal components of the portrait features and k-means clustering was used to distribute neuronal spikes into various clusters. Finally, similar clusters were iteratively merged based upon inter-cluster portrait differences.
尖峰信息有助于将神经元活动与各种刺激相关联，或确定用于深部脑刺激的目标神经区域。基于神经元尖峰特征的数据聚类提供了一种分离来自不同神经元的尖峰的方法。然而，由于尖峰变形或噪声干扰，一些尖峰对齐不正确，从而降低了尖峰分类的准确性。在本研究中，我们提出了在神经元尖峰的重构相空间上进行无监督尖峰分类，其中导出的相空间图受对齐偏差的影响较小。主成分分析用于提取图特征的主要主成分，k均值聚类用于将神经元尖峰分配到不同的簇中。最后，基于簇间图差异对相似的簇进行迭代合并。
