  Deep Learning (DL) is a two-step classification model that consists feature
learning, generating feature representations using unsupervised ways and the
supervised learning stage at the last step of model using at least two hidden
layers on the proposed structures by fully connected layers depending on of the
artificial neural networks. The optimization of the predefined classification
parameters for the supervised models eases reaching the global optimality with
exact zero training error. The autoencoder (AE) models are the highly
generalized ways of the unsupervised stages for the DL to define the output
weights of the hidden neurons with various representations. As alternatively to
the conventional Extreme Learning Machines (ELM) AE, Hessenberg
decomposition-based ELM autoencoder (HessELM-AE) is a novel kernel to generate
different presentations of the input data within the intended sizes of the
models. The aim of the study is analyzing the performance of the novel Deep AE
kernel for clinical availability on electroencephalogram (EEG) with stroke
patients. The slow cortical potentials (SCP) training in stroke patients during
eight neurofeedback sessions were analyzed using Hilbert-Huang Transform. The
statistical features of different frequency modulations were fed into the Deep
ELM model for generative AE kernels. The novel Deep ELM-AE kernels have
discriminated the brain activity with high classification performances for
positivity and negativity tasks in stroke patients.
