Journal of neuroscience methods
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J. Neurosci. Methods · May 2007
Automated optimal detection and classification of neural action potentials in extra-cellular recordings.
Determination of single unit spikes from multiunit spike trains plays a critical role in neurophysiological coding studies which require information about the precise timing of events underlying the neural codes that are the basis of behavior. Searching for optimal spike detection strategies has therefore been the focus of many studies over the past two decades. In this study we describe and implement an algorithm for the optimal real time detection and classification of neural spikes. ⋯ The third step uses these templates for the real time spike detection and classification. In this step the incoming data are projected into a lower dimensional space that is designed to maximally separate the signal from the noise energy. This algorithm provides an accurate estimate of the signal to noise ratio and provides an accurate estimate of spike times and spike shapes.