  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.
