Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke

Year 2016, , 205 - 210, 01.12.2016

Gokhan Altan , Yakup Kutlu Novruz Allahverdi

https://doi.org/10.18100/ijamec.270307

Cited By: 27

Abstract

An electroencephalogram (EEG) is an electrical activity which is
recorded from the scalp over the sensorimotor cortex during vigilance or
sleeping conditions of subjects.  It can
be used to detect potential problems associated with brain disorders.  The aim of this study is assessing the
clinical usefulness of EEG which is recorded from slow cortical potentials
(SCP) training in stroke patients using Deep belief network (DBN) which has a
greedy layer wise training using Restricted Boltzmann Machines based
unsupervised weight and bias evaluation and neural network based supervised
training. EEGs are recorded during eight SCP neurofeedback sessions from two
stroke patients with a sampling rate of 256 Hz. All EEGs are filtered with a
low pass filter. Hilbert-Huang Transform is applied to the trails and various
numbers of Instinct Mode Functions (IMFs) are obtained. High order statistics
and standard statistics are extracted from IMFs to create the dataset. The
proposed DBN-based brain activity classification has discriminated positivity
and negativity tasks in stroke patients and has achieved high rates of 90.30%,
96.58%, and 91.15%, for sensitivity, selectivity, and accuracy, respectively.

Keywords

Deep Belief Networks, SCP, Slow Cortical potentials, Hilbert-Huang Transform, EEG

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