Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis

TL;DR

This paper introduces a novel Deep Autoencoder kernel based on Hessenberg decomposition for EEG analysis in stroke patients, demonstrating high classification accuracy in neurofeedback tasks.

Contribution

The study proposes a new Deep ELM-AE kernel for EEG data analysis, enhancing classification performance in stroke patient brain activity detection.

Findings

High classification accuracy for brain activity in stroke patients

Effective discrimination of positivity and negativity tasks

Potential clinical application for EEG analysis

Abstract

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.