Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders

• URL: http://arxiv.org/abs/2109.08945v1
• Date: Sat, 18 Sep 2021 14:51:24 GMT
• Title: Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders
• Authors: Christodoulos Kechris, Alexandros Delitzas, Vasileios Matsoukas, Panagiotis C. Petrantonakis
• Abstract summary: We propose 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.
• Score: 62.997667081978825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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