BRAIN CONNECTIVITY: A COMPARISON BETWEEN PARALYSIS AND PRE-PARALYSIS

Author: Pratishtah Sobrun

Sobrun, Pratishtah, 2017 BRAIN CONNECTIVITY: A COMPARISON BETWEEN PARALYSIS AND PRE-PARALYSIS , Flinders University, School of Computer Science, Engineering and Mathematics

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Abstract

Electroencephalogram (EEG) is a method of recording the electrical activity of the brain. This technique has been widely used to understand the behaviour of the brain or as a tool for diagnosis to certain neurological conditions. However, the EEG signal was always found contaminated with muscle activity. Having muscle activity in the EEG signal can be a major drawback when studying the causal interactions of different areas of the brain also termed as the effective connectivity of the brain. A comparison between the effective connectivity paralysed state and pre-paralysed state shows that the brain behaves quite differently when the muscles are paralysed. As a consequence, outstanding results are obtained. It was seen that the brain tends to be more synchronised when the muscles are in a paralysed state and that the frontal lobe, motor control and the auditory cortex are the areas with highest information flow. On the other hand, in the pre-paralysis state, a considerable amount of disturbances was found and concluded to be muscle activity. To obtain those results, two sets of tests were done, namely, the head muscle task and the oddball task. The connectivity calculation of the head muscle task had the aim of triggering the masseter muscle, frontalis muscle and the paraspinal muscle whereas the oddball task is a simple auditory task. The oddball task provides a good comparison to whether muscle activity has an influence on the effective brain connectivity. This study confirms that brain connectivity should be calculated from EEG data that had noise removed or should be measured directly from the brain, not the scalp.

Keywords: Brain connectivity, EEG, paralysis, muscle activity
Subject: Engineering thesis

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