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A lot of methods were created in last decade for the spatio-temporal analysis of multi-electrode array (MEA) neuronal data sets. All these methods were implemented starting from a channel to channel analysis, with a great computational effort and  onerous spatial pattern recognition task. &#xd;&#xa;Our idea is to approach the MEA data collection from a different point of view, i.e. considering all channels simultaneously. We transform the 2D plus time MEA signal in a mono-dimensional plus time signal and elaborate it as a normal 1D signal, using the Space-Amplitude Transform method. &#xd;&#xa;This geometrical transformation is completely invertible and allows to employ very fast processing algorithms. &#xd;&#xa

A new approach to the spatio-temporal pattern identification in neuronal multi-electrode registrations