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Improving electrocorticograms of awake and anaesthetized mice using wavelet denoising

Abstract The quality of bioelectrical signals is essential for functional evaluation of cellular circuits. The electrical activity recorded from the cortical brain surface represents the average of many individual synaptic processes. By downsizing micro-electrode arrays, the spatial resolution of electrocortico-grams (ECoGs) can be increased. But, upon increasing electrode impedance, recorded noise from the electrode-tissue interface and the surroundings will become more prominent. Frequently, signal interpretation is improved by post-processing using filtering or pattern recognition. For a variety of applications, wavelet denoising has become an accepted tool. Here, we present how wavelet denoising affects the signal-to-noise ratio of ECoGs. The recording qualities from awake and anesthetized mice was artificially reduced by adding two noise models prior to filtering. Raw and filtered signals were compared by calculating the linear correlation coefficient.