Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders
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.
Cite
@article{arxiv.2109.08945,
title = {Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders},
author = {Christodoulos Kechris and Alexandros Delitzas and Vasileios Matsoukas and Panagiotis C. Petrantonakis},
journal= {arXiv preprint arXiv:2109.08945},
year = {2021}
}
Comments
Accepted version to be published in the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021)