Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders

Christodoulos Kechris, Alexandros Delitzas, Vasileios Matsoukas, Panagiotis C. Petrantonakis

Abstract: Extracellular recordings are severely contaminated by a considerable amount of noise sources, rendering the denoising process an extremely challenging task that should be tackled for efficient spike sorting. To this end, we propose an end-to-end deep learning approach to the problem, utilizing a Fully Convolutional Denoising Autoencoder, which learns to produce a clean neuronal activity signal from a noisy multichannel input. The experimental results on simulated data show that our proposed method can improve significantly the quality of noise-corrupted neural signals, outperforming widely-used wavelet denoising techniques.