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Keywords: Spike detection; Spike sorting; Spike train analysis; Extra-cellular recording; Tetrode; Electrophysiology; Multi-unit recording; Single-unit recording; Cluster analysis; Superposition resolution; MATLAB; Accuracy quantification; Object Oriented Programming;
Data simulation; Neuroshare Native
 
     
 

FeatureExtract Tool                     



Summary

The FeatureExtract tool is the second step in analyzing spike train data. You’ll first use one of the Spike Detection tools, such as BasicThresh or WaterfillThresh, to detect segments of data that are likely to contain neural event spikes. Next, add the FeatureExtract tool to the NeuroMAX tool chain to extract features such as spike height and principle components from each candidate fragment. These features are used by the “cluster” tools, such as KMeansCluster, to identify groups of spikes with the same features.

Screen Shot




Details

FeatureExtract reduces the dimensionality of a fragment data set by extracting the features to represent each fragment in the clustering operation. Currently, the available features include positive and negative peak height, and total energy. Adding your own feature to the NeuroMAX feature library is as easy as writing a simple MATLAB function to compute and return the desired feature.

 
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Last Updated: 07-Apr-2008