Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders
| Autor: | Christodoulos Kechris, Alexandros Delitzas, Vasileios Matsoukas, Panagiotis C. Petrantonakis |
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| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Quantitative Biology::Neurons and Cognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Data_CODINGANDINFORMATIONTHEORY Signal-To-Noise Ratio Machine Learning (cs.LG) Protein Transport Cell Movement Quantitative Biology - Neurons and Cognition Computer Science::Computer Vision and Pattern Recognition FOS: Biological sciences FOS: Electrical engineering electronic engineering information engineering Neurons and Cognition (q-bio.NC) Neural Networks Computer Electrical Engineering and Systems Science - Signal Processing Noise |
| DOI: | 10.48550/arxiv.2109.08945 |
| Popis: | 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. Comment: Accepted version to be published in the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021) |
| Databáze: | OpenAIRE |
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