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 the 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 columns of the rat somatosensory cortex during whisking provide information about the tactile information processing pathway. Particularly when large-scale high-resolution neuronal probes are used, during each experiment many single LFPs are recorded as an outcome of the underlying neuronal network activation and averaged to extract information. However, single LFP signals are frequently very different from each other. Extracting information provided by their shape can be used to better decode information transmitted by the network. This work proposes an automated method capable of classifying these signals based on their shapes. A template matching approach is used to recognize single LFPs and the contour information is extracted from the recognized signals to generate a feature matrix, which is then classified using intelligent K-means clustering. As an application example, the shape-specific information (e.g., latency and amplitude) of LFPs evoked in the rat barrel cortex are used in decoding the rat whisker information processing pathway using the proposed method.