Abstract
We are exploring new methods of spike detection to improve spike-sorting in tetrode recordings. Based on our observation that the four channels of the tetrode carry highly correlated signals, we propose the use of a hyperellipsoidal thresholding surface in the four-dimensional space of the signal values to detect spikes. This surface is determined by the cross-channel covariance matrix and provides a better approximation of the equiprobable surface of the noise amplitude distribution compared to the traditionally used hypercubical thresholding surface. This spike detection procedure greatly improves the separation of signal clusters from the noise cluster around the origin. We have extended these approaches to automatic spike-sorting in both amplitude and full waveform spaces.
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