EEG analysis: Brain connectivity in stroke

Author: Turki Alrobaian

Alrobaian, Turki, 2017 EEG analysis: Brain connectivity in stroke, Flinders University, School of Computer Science, Engineering and Mathematics

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Today in the modernization world everything has become so simple in one click of

button, but this fast living life and changes in way of living has led to some major

problems. Brain stroke is one of the leading problems in today’s middle or elder age

people. Science has progressed a lot and looks for many network analysis to deal with

such problems. But still it’s a field in which many unfolds still exist which creates a

hinder to successfully unturned the issue. Brain consists of 100 billion neurons and

one trillion gila. This network of brain stores experiences and houses consciousness.

Focal brain lesions affect multiple network properties simultaneously and how

changes on smaller scales influence those on larger scales.

Although understanding of the brain was enhanced by the stroke, stroke is one of the

biggest killers and a leading cause of disability. Stroke describes damage to the

neuraxis (the brain and spinal cord), resulting from an abnormality in cerebral blood

supply. It is a neurological disorder characterized by interrupted or insufficient blood

supply to certain parts of the brain. It is a significant cause of mortality and morbidity.

The main causes of ischemic stroke in large blood vessels are building up of abnormal

fatty lumps on the inner lining of arteries supplying blood to the brain, infections such

as valvular diseases, ischemic heart disease and diabetes that narrow cerebral blood

vessels or abnormal heart rhythms. In small cerebral blood vessels, fatty lumps on the

inner walls of the arteries could break off and transported by the bloodstream to lodge

in the narrower blood vessels in the brain causing embolic occlusion.

The main causes of leaking or rapturing of weak blood vessels in hemorrhagic stroke

are aneurysm and arteriovenous malformations. Aneurysm results from a weakened

part of a blood vessel ballooning. If unattended, the region continues to balloon and

weaken until it raptures and releases blood into the brain. Arteriovenous

malformations refer to a group of abnormally formed blood vessels. Any of these

blood vessel can rapture and release blood into the brain cavity. Other causes of

rapturing of weak cerebral blood vessels are cranial (head) trauma, an abnormal

accumulation of blood in the cranial cavity, disorders such as hypertension and

cerebral amyloid angiopathy tumors in the brain, the use of therapeutic

anticoagulation (blood-thinning medication) or bleeding disorders that cause a

considerable reduction in platelets.

The motivation for this work includes an electroencephalogram (EEG)-based

experiment on the performance of several mental tasks including reading, auditory,

subtraction and finger tapping among others in 9 patients with multilateral stroke, the

main challenge was to analyse that the anatomic lesion affects the functional brain

network on multiple levels.

In the past few years a large number of studies in the specific field have been

developed. A broad range of detecting follows the morphological based methods.

However, many of the methods, proposed previously failed to give good performance

at the output. Sahil Bajaj [2015] [35] proposed a method which attracts the field in

new limelight. In the paper, the author’s aims at primary motor area during motor

imagination (MI) task and how this differs during motor execution (ME) task are still

questions of interest.

A new scheme to make some improvement on this is to explore the application of

Normalised Transfer Entropy (NTE) measure to construct EEG based directed brain

network. Along with this, the bedrock is to quantify and plot the network for

statistically important connections for both stroke and healthy subjects and to

determine information flow patterns during different tasks.

Keywords: Brain Connectivity, electroencephalogram, EEG, Stroke, Normalised Transfer Entropy

Subject: Engineering thesis

Thesis type: Masters
Completed: 2017
School: School of Computer Science, Engineering and Mathematics
Supervisor: Kenneth Pope