Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN AutoEncoder

TL;DR

This paper introduces a novel fractional and compressed CNN autoencoder that transforms EEG signals into an orthogonal domain for improved denoising, especially against muscle artifacts, outperforming existing methods.

Contribution

It proposes a fractional-order CNN autoencoder with orthogonal domain transformation and parameter compression, enhancing EEG denoising performance over prior techniques.

Findings

Improved signal quality with tuned fractional order $oldsymbol{ extalpha}$

Effective noise reduction on standard EEG datasets

Outperforms existing denoising methods

Abstract

This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts (MA), introduced by the movement of muscles. The existing EEG denoising methods make use of decomposition, thresholding and filtering techniques. In the proposed approach, EEG signals are first transformed to orthogonal domain using Tchebichef moments before feeding to the proposed architecture. A new hyper-parameter ($\alpha$) is introduced which refers to the fractional order with respect to which gradients are calculated during back-propagation. It is observed that by tuning $\alpha$, the quality of the restored signal improves significantly. Motivated by the high usage of portable low energy devices which make use of compressed deep learning architectures, the trainable parameters of the proposed architecture are compressed using randomized singular value decomposition (RSVD) algorithm. The experiments are performed on the standard EEG datasets, namely, Mendeley and Bonn. The study shows that the proposed fractional and compressed architecture performs better than existing state-of-the-art signal denoising methods.