An Automated Classification Method for Single Sweep Local Field Potentials Recorded from Rat Barrel Cortex under Mechanical Whisker Stimulation

Understanding brain signals as an outcome of brain’s information processing is a challenge for the neuroscience and neuroengineering community. Rodents sense and explore the environment through whisking. The local field potentials (LFPs) recorded from the barrel 28 columns of the rat somatosensory cortex (S1) during whisking provide information about the tactile information processing pathway. Particularly when using large-scale high-resolution neuronal probes, during each experiment many single LFPs are recorded as an outcome of underlying neuronal network activation and averaged to extract information. However, single LFP signals are frequently very different from each other and extracting information provided by their shape is a useful way to better decode information transmitted by the network. In this work, we propose an automated method capable of classifying these signals based on their shapes. We used template matching approach to recognize single LFPs and extracted the contour information from the recognized signal to generate a feature matrix, which is then classified using the intelligent K–means clustering. As an application example, shape specific information (e.g., latency, and amplitude) of LFPs evoked in the rat somatosensory barrel cortex and used in decoding the rat whiskers information processing pathway is provided by the method.