Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders
This addresses noise removal in neural recordings for spike sorting, but it is incremental as it applies a known deep learning method to a specific domain problem.
The paper tackled denoising extracellular neural recordings, which are heavily contaminated by noise, by proposing a fully convolutional denoising autoencoder that learns to produce clean signals from noisy multichannel inputs, and it outperformed wavelet denoising techniques on simulated data.
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.