Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis

by   Gökhan Altan, et al.

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.


page 1

page 2

page 3

page 4


Superiorities of Deep Extreme Learning Machines against Convolutional Neural Networks

Deep Learning (DL) is a machine learning procedure for artificial intell...

Letters of the Alphabet: Discovering Natural Feature Sets

Deep learning networks find intricate features in large datasets using t...

Unsupervised 3D Object Learning through Neuron Activity aware Plasticity

We present an unsupervised deep learning model for 3D object classificat...

Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation

Brain-computer interfaces (BCIs) enable direct communication between hum...

Deep Multiple Kernel Learning

Deep learning methods have predominantly been applied to large artificia...

End-to-end training of deep kernel map networks for image classification

Deep kernel map networks have shown excellent performances in various cl...

Deep regularization and direct training of the inner layers of Neural Networks with Kernel Flows

We introduce a new regularization method for Artificial Neural Networks ...

Please sign up or login with your details

Forgot password? Click here to reset