  A fractional-based compressed auto-encoder architecture has been introduced
to solve the problem of denoising electroencephalogram (EEG) signals. The
architecture makes use of fractional calculus to calculate the gradients during
the backpropagation process, as a result of which a new hyper-parameter in the
form of fractional order ($\alpha$) has been introduced which can be tuned to
get the best denoising performance. Additionally, to avoid substantial use of
memory resources, the model makes use of orthogonal features in the form of
Tchebichef moments as input. The orthogonal features have been used in
achieving compression at the input stage. Considering the growing use of low
energy devices, compression of neural networks becomes imperative. Here, the
auto-encoder's weights are compressed using the randomized singular value
decomposition (RSVD) algorithm during training while evaluation is performed
using various compression ratios. The experimental results show that the
proposed fractionally compressed architecture provides improved denoising
results on the standard datasets when compared with the existing methods.
