Date of Award

5-31-2020

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering - (M.S.)

Department

Electrical and Computer Engineering

First Advisor

Joerg Kliewer

Second Advisor

Antje Ihlefeld

Third Advisor

Ali Abdi

Abstract

Electroencephalograph(EEG) is a process mainly used in medical and research fields to study the electrical activities in a brain. In this technique, 128 or 256 electrodes are attached to the scalp and the electrical activities of the human brain is recorded with the help of a software. In the global scenario, the EEG responses are studied and analysed to acknowledge any disorders in the brain, such as epilepsy or head injury.

Recent studies performed by researchers, have focused on analysing these electrical activities to access perceived audio quality from users by using information theoretic approaches, such as mutual information. Experiments were conducted by using a total of 128 electrodes, comprised in 8 regions of interests. In the research, each region is considered to have 9 electrodes each. The aim of this thesis was to build on previous research, and to reduce the use of the number of electrodes, so as to reduce the cost and complexity of the setup. In order to achieve this, first both the good and the bad quality audios were analyzed, and a receiver operating characteristic curve is plotted to draw a classification. Furthermore, a combination of different regions were taken and their mutual information were calculated, in order to check which group of regions give us the best result. This test is undertaken for each available combination of 2 or 3 regions. The classification accuracy was obtained by a variance detector approach and the accuracy was verified by computing z scores.

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