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Frequency Recognition of Selected Features of EEG Signals with Neuro-Statistical Method

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dc.contributor.advisor Shahjahan, Prof. Dr. Md.
dc.contributor.author Hossain, Md. Zakir
dc.date.accessioned 2018-08-11T06:57:52Z
dc.date.available 2018-08-11T06:57:52Z
dc.date.copyright 2014
dc.date.issued 2014-04
dc.identifier.other ID 0000000
dc.identifier.uri http://hdl.handle.net/20.500.12228/312
dc.description This thesis is submitted to the Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Electrical and Electronic Engineering, April 2014 en_US
dc.description Cataloged from PDF Version of Thesis.
dc.description Includes bibliographical references (pages 57-62).
dc.description.abstract The advent of research work to analyze massive multiway oriented electroencephalogram (EEG) signals with low configurable computer is a great challenge. This thesis presents an algorithm for extracting underlying frequency components of such EEG data. Frequency components of these data play a vital role to realize brain-body condition. Usually, a huge amount of time and specially built computers are essential to process these EEG data having different subjects. It also restricts to visualize inherent frequency of EEG for a general practitioner. An algorithm is developed using two-stage cascaded architecture of canonical correlation analysis with neural network named neural canonical correlation analysis (NCCA) to address three major challenges for extracting frequency components from EEG data, such as: (a) It processes massive data which are feed sequentially into neural network, rather than feeding whole data at a time, (b) It uses the conventional personal computer instead of special computer built for such application, (c) It spends very short time for a moderate data set consisting of several ways (time, trials and channels). (d) It considers the nonlinear correlation among the data groups while statistical CCA ignores it. In order to get reliable and robust result, the experimental are carried out with different structures of network such as linear, nonlinear and nonlinear feedback structures. The inherent dominant frequency of 1 Hz having a quite resemblance with EEG landscape has been found. This provides a great opportunity in analyzing brain-body function. Although it is possible to recognize frequency of massive EEG data at shorter time with NCCA than statistical CCA, but subjects differentiation is still a great challenge. In this view, this paper presents a new feature selection (FS) approach based on NCCA. In order to get robust features subset having maximum correlation and minimum redundancies, NCCA is devised to search highly correlated subsets by maximizing correlation among several subdivisions of raw data and pruning the features of lightly scored weights of CCA network. The result of NCCA is very robust in terms of accuracy. In this sense, frequency recognition is very easy using selected EEG features than original features which are inspected from correlation profiles. The computational complexity is also greatly reduced if selected features are used to recognize frequency which is proved theoretically and experimentally. In this connection, elapsed time is calculated and observed that NCCA is about 2 to 33 times faster to recognize frequencies from selected EEG features than original set. en_US
dc.description.statementofresponsibility Md. Zakir Hossain
dc.format.extent 62 pages
dc.language.iso en_US en_US
dc.publisher Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh. en_US
dc.rights Khulna University of Engineering & Technology (KUET) thesis/ dissertation/internship reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subject Frequency en_US
dc.subject Neuro-Statistical Method en_US
dc.subject Electroencephalographic (EEG) en_US
dc.subject Neural Networks en_US
dc.title Frequency Recognition of Selected Features of EEG Signals with Neuro-Statistical Method en_US
dc.type Thesis en_US
dc.description.degree Master of Science in Electrical and Electronic Engineering
dc.contributor.department Department of Electrical and Electronic Engineering

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