Dynamic Bayesian Networks For Eeg Motor Imagery Feature Extraction / (Record no. 8844)

MARC details
000 -LEADER
fixed length control field 10998nam a22002417a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210112b2018 a|||f mb|| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency EG-CaNU
Transcribing agency EG-CaNU
041 0# - Language Code
Language code of text eng
Language code of abstract eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 610
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Basem El Asioty
245 1# - TITLE STATEMENT
Title Dynamic Bayesian Networks For Eeg Motor Imagery Feature Extraction /
Statement of responsibility, etc. Basem El Asioty
300 ## - PHYSICAL DESCRIPTION
Extent 111 p.
Other physical details ill.
Dimensions 21 cm.
500 ## - GENERAL NOTE
Materials specified Supervisor: Seif Eldawlatly
502 ## - Dissertation Note
Dissertation type Thesis (M.A.)—Nile University, Egypt, 2018 .
504 ## - Bibliography
Bibliography "Includes bibliographical references"
505 0# - Contents
Formatted contents note Contents:<br/>Chapter 1 - INTRODUCTION ............................................................................1<br/>1.1 Overview ......................................................................................................... 1<br/>1.2 Objectives ........................................................................................................ 2<br/>1.3 Contributions ................................................................................................... 2<br/>1.4 Thesis Organization .......................................................................................... 3<br/>Chapter 2 - BRAIN-COMPUTER INTERFACE ......................................................5<br/>2.1 Introduction .................................................................................................... 5<br/>2.2 BCI Measurement Techniques .......................................................................... 6<br/>2.3 Electroencephalography (EEG) .......................................................................... 8<br/>2.4 EEG Rhythms ................................................................................................. 10<br/>2.4.1 Delta Rhythms .................................................................................................. 10<br/>2.4.2 Theta Rhythms ................................................................................................. 11<br/>2.4.3 Alpha Rhythms ................................................................................................. 11<br/>2.4.4 Mu Rhythms ..................................................................................................... 11<br/>2.4.5 Beta Rhythms ................................................................................................... 11<br/>2.4.6 Gamma Rhythms ............................................................................................. 11<br/>2.5 EEG Control Signals used for BCI ..................................................................... 12<br/>2.5.1 P300 Signal....................................................................................................... 12<br/>2.5.2 Sensorimotor Rhythms ..................................................................................... 13<br/>2.5.3 Visual Evoked Potential (VEP) .......................................................................... 14<br/>2.6 EEG Processing Techniques for BCI ................................................................. 15<br/>2.6.1 Preprocessing ................................................................................................... 15<br/>2.6.2 Feature Extraction ............................................................................................ 19<br/>2.6.3 Classification .................................................................................................... 23<br/>2.7 BCI Applications ............................................................................................. 24<br/>2.8 Summary ....................................................................................................... 28<br/>Chapter 3 - BRAIN CONNECTIVITY ANALYSIS ................................................. 29<br/>3.1 Introduction .................................................................................................. 29<br/>3.2 The Concept of Brain Connectivity .................................................................. 29<br/>3.3 Connectivity Analysis Methods ....................................................................... 32<br/>3.3.1 Functional Connectivity Methods .................................................................... 32<br/>3.3.2 Effective Connectivity Methods ....................................................................... 35<br/>3.4 Connectivity Features for BCI Applications ...................................................... 38<br/>3.5 Summary ....................................................................................................... 40<br/>Chapter 4 - DYNAMIC BAYESIAN NETWORKS FOR BRAIN CONNECTIVITY ....... 41<br/>4.1 Introduction .................................................................................................. 41<br/>viii<br/>4.2 Bayesian Network Principles .......................................................................... 41<br/>4.3 Learning ........................................................................................................ 44<br/>4.3.1 Network Structure Learning ............................................................................. 44<br/>4.3.2 Parameter Learning ......................................................................................... 46<br/>4.4 Inference with Bayesian Network ................................................................... 47<br/>4.5 Dynamic Bayesian Network (DBN) .................................................................. 47<br/>4.6 DBN for Brain Connectivity ............................................................................. 49<br/>4.7 Summary ....................................................................................................... 51<br/>Chapter 5 - EXPERIMENTAL METHODS .......................................................... 52<br/>5.1 Introduction .................................................................................................. 52<br/>5.2 Dataset: BCI Competition 2008 – Graz Dataset A (BCIIV2a) .............................. 53<br/>5.3 Experimental Setup ........................................................................................ 53<br/>5.4 Data Recording .............................................................................................. 54<br/>5.5 Building the Training Data .............................................................................. 55<br/>5.6 Preprocessing ................................................................................................ 56<br/>5.6.1 Spatial Filtering ................................................................................................ 56<br/>5.6.2 Frequency Band Filtering ................................................................................. 56<br/>5.6.3 Discretization ................................................................................................... 57<br/>5.7 Building Dynamic Bayesian Network............................................................... 58<br/>5.8 Banjo Toolbox ................................................................................................ 60<br/>5.9 Input Data ..................................................................................................... 60<br/>5.10 Banjo Experimental Setup ............................................................................ 61<br/>5.11 Output Data (matrix, graph) ......................................................................... 62<br/>5.12 Dimensionality Reduction ............................................................................ 62<br/>5.13 Combining with Band Power Features .......................................................... 63<br/>5.14 SVM Classifier .............................................................................................. 63<br/>5.15 Granger Causality Connectivity and DBN Connectivity Comparison ................ 64<br/>5.16 Summary ..................................................................................................... 65<br/>Chapter 6 - RESULTS ...................................................................................... 66<br/>6.1 Introduction .................................................................................................. 66<br/>6.2 Frequency Band for Connectivity Features ...................................................... 66<br/>6.3 Connectivity Networks ................................................................................... 68<br/>6.4 Combining Band Power and Connectivity Features ......................................... 69<br/>6.5 Granger Causality Connectivity and DBN Connectivity Comparison .................. 73<br/>6.6 DBN Connectivity Analysis Discussion ............................................................. 75<br/>6.7 Summary ....................................................................................................... 76<br/>Chapter 7 - CONCLUSION AND FUTURE WORK .............................................. 77<br/>7.1 Summary and Contributions ........................................................................... 77<br/>7.2 Future Work .................................................................................................. 78<br/>PUBLICATION ................................................................................................. 80<br/>REFERENCES .......................
520 3# - Abstract
Abstract Abstract:<br/>Brain-Computer Interface (BCI) is a system that establishes a communication channel to enable the use of brain activity to interface and control computer systems. One of the most challenging applications of BCI is to identify movement direction from electroencephalography (EEG) data by developing models that discriminate between different motor imagery tasks such right hand movement versus left hand movement. Herein, we utilize graph-based tools to enhance the performance of motor imagery BCIs. Specifically, we introduce the use of Dynamic Bayesian Networks (DBNs) as efficient graphical tool that could be used to detect causal relationships among EEG electrodes during motor imagery tasks. We inferred the causal relationships between EEG electrodes during each of right and left hands imagery movements from 9 different subjects. We demonstrate how using the inferred connectivity as a feature enhances the discrimination among right and left hands imagery movements compared to using traditional band power features. Our analysis reveals a distinctive connectivity pattern manifested by an increase in the number of incoming connections to the right hemisphere motor area compared to the left hemisphere during right hand imagery movements. This pattern is reversed during left hand imagery movements. We demonstrate also using other connectivity features such as Granger Causality connectivity features for the same classification problem. Our results demonstrate how DBN connectivity features give better classification accuracy compared to Granger Causality.
546 ## - Language Note
Language Note Text in English, abstracts in English .
650 #4 - Subject
Subject Informatics-IFM
655 #7 - Index Term-Genre/Form
Source of term NULIB
focus term Dissertation, Academic
690 ## - Subject
School Informatics-IFM
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
650 #4 - Subject
-- 266
655 #7 - Index Term-Genre/Form
-- 187
690 ## - Subject
-- 266
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total Checkouts Full call number Date last seen Price effective from Koha item type
    Dewey Decimal Classification   Not For Loan Main library Main library 01/12/2021   610 / B.E.D / 2018 01/12/2021 01/12/2021 Thesis