Methodologies for pattern extraction and analysis from neural activity data captured by simultaneous sensors, are gaining major interest due to technological advances in sensor devices. From early Electroencephalography (EEG), able to capture a few simultaneous signals, to in-vivo spinal recording, Magneto Encephalograpy (MEG) or functional Magnetic Resonance Imaging (fMRI), that capture up to hundreds of signals, the amount of data from neurophysiological experimentation to be analyzed multiplies every year.

This paper proposes a methodology composed by several steps. It is targeted to extract qualitative and quantitative information from experiments recorded using such devices. The process selects patterns of interest for the hypothesis in the experiment, analyzes the characteristics of the patterns and its changes in temporal behavior from the perspective of individual sensors, identifies coocurrences of patterns from the simultaneous sensors and extracts frequent temporal patterns that show recurrent collective sensor activity. These patterns are expected to characterize and discriminate different states or conditions in the experiments.

An application to the analysis of recordings of spinal neural activity in cats is presented to show the use of the methodology.