  For the past few years, we have developed flexible, active, multiplexed
recording devices for high resolution recording over large, clinically relevant
areas in the brain. While this technology has enabled a much higher-resolution
view of the electrical activity of the brain, the analytical methods to
process, categorize and respond to the huge volumes of seizure data produced by
these devices have not yet been developed. In this work we proposed an
unsupervised learning framework for spike analysis, which by itself reveals
spike pattern. By applying advanced video processing techniques for separating
a multi-channel recording into individual spike segments, unfolding the spike
segments manifold and identifying natural clusters for spike patterns, we are
able to find the common spike motion patterns. And we further explored using
these patterns for more interesting and practical problems as seizure
prediction and spike wavefront prediction. These methods have been applied to
in-vivo feline seizure recordings and yielded promising results.
