Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders

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 Ful...

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Bibliographic Details
Published in:2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2021; pp. 890 - 893
Main Authors: Kechris, Christodoulos, Delitzas, Alexandros, Matsoukas, Vasileios, Petrantonakis, Panagiotis C.
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.11.2021
Subjects:
Brain modeling
Cell Movement
Convolution
Discrete wavelet transforms
Extracellular
Neural Networks, Computer
Noise
Noise measurement
Noise reduction
Protein Transport
Signal-To-Noise Ratio
Task analysis
ISSN:2694-0604, 2694-0604
Online Access:Get full text
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Summary: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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ISSN:2694-0604
2694-0604
DOI:10.1109/EMBC46164.2021.9630585