Analysis of EEG Signals For EEG-based Brain-Computer 228631/ ¢  Computer Interface(BCI)

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  • Analysis of EEG Signals

    For

    EEG-based Brain-Computer Interface

    Jessy Parokaran Varghese

    School of Innovation, Design and Technology Mälardalen University

    Vasteras, Sweden

    Supervisor and Examiner: Dr. BARAN ÇÜRÜKLÜ

    Date: July 2009

  • 2   

    Abstract

    Advancements in biomedical signal processing techniques have led Electroencephalography

    (EEG) signals to be more widely used in the diagnosis of brain diseases and in the field of Brain

    Computer Interface(BCI). BCI is an interfacing system that uses electrical signals from the brain

    (eg: EEG) as an input to control other devices such as a computer, wheel chair, robotic arm etc.

    The aim of this work is to analyse the EEG data to see how humans can control machines using

    their thoughts.In this thesis the reactivity of EEG rhythms in association with normal, voluntary

    and imagery of hand movements were studied using EEGLAB, a signal processing toolbox

    under MATLAB. In awake people, primary sensory or motor cortical areas often display 8-12

    Hz EEG activity called ’Mu’ rhythm ,when they are not engaged in processing sensory input or

    produce motor output. Movement or preparation of movement is typically accompanied by a

    decrease in this mu rhythm called ’event-related desynchronization’(ERD). Four males, three

    right handed and one left handed participated in this study. There were two sessions for each

    subject and three possible types : Imagery, Voluntary and Normal. The EEG data was sampled

    at 256Hz , band pass filtered between 0.1 Hz and 50 Hz and then epochs of four events : Left

    button press , Right button press, Right arrow ,Left arrow were extracted followed by baseline

    removal.After this preprocessing of EEG data, the epoch files were studied by analysing Event

    Related Potential plots, Independent Component Analysis, Power spectral Analysis and Time-

    Frequency plots. These analysis have shown that an imagination or a movement of right hand

    cause a decrease in activity in the hand area of sensory motor cortex in the left side of the brain

    which shows the desynchronization of Mu rhythm and an imagination or a movement of left

    hand cause a decrease in activity in the hand area of sensory motor cortex in the right side of the

    brain. This implies that EEG phenomena may be utilised in a Brain Computer Interface operated

    simply by motor imagery and the present result can be used for classifier development and BCI

    use in the field of motor restoration.

  • 3   

    Preface

    I would like to sincerely thank the EURECA programme and Erasmus Mundus

    External Cooperation window and Prof Dr. Sasikumar Punnekatt, the EURECA

    Co-ordinator for selecting me and giving me an opportunity to learn in Europe.

    Sincere thanks to Malardalen University for providing me with all the facilities

    and guidance needed for my thesis work here. I also thank Amrita University and

    Dr.Narayanankutty for permitting me to do my final year project at Malardalen

    University.

    I sincerely want to thank my supervisor Dr. Baran Cürüklü for providing me with

    his valuable guidance irrespective of his busy schedule and gave me the chance to

    do this wonderful experience that will help me all my life. I also want to give my

    sincere thanks to Ozgur Ali Argunshah who gave me guidelines from Sabancci

    University, Turkey, for analyzing the EEG signals.

  • 4   

    Table of Contents 1 Introduction 6

    1.1 Aim and Scope of the Thesis 6 1.2 Terminology 6

    2 Biological Back ground 7 2.1 Human Brain 7 2.2 Sensory Motor Cortex 8 2.3 Brain waves and EEG 8

    3 Brain Computer Interface 9 3.1 Definition and Applications of BCI 9 3.2 Principle of BCI 9 3.3 Types of BCI 10 3.4 Brain Signals for BCI 11 4 Related Work 12 4.1 ERS/ERD of Mu and Beta rhythm during Mental Imagery 12 4.2 Classification of EEG data for BCI 15 5 Problem Formulation 16 6 Method and materials 16 6.1 Subjects 16 6.2 Experimental Paradigm and EEG Recording 16 6.3 Preprocessing of EEG Data 17 7 Analysis of EEG Signals 19 7.1 Power Spectrum Analysis 19 7.2 Event Related Potential (ERP) plots 19 7.2.1 Channel ERP Image plots 19 7.2.2 ERP Comparisons 20 7.3 Time frequency Analysis 20 7.4 Independent Component Analysis 21 7.5 Component Clustering 22 7.6 Dipole localization 22

  • 5   

    8 Results 23 8.1 Power Spectral Plots 23 8.2 Channel ERP Image plots 24 8.3 Comparison of channel ERPs 26 8.4 Time frequency Plots 27 8.4.1. ERSP plots 27 8.4.2 Time frequency decompositions on all electrodes 29 8.5 Independent Component Clustering 30 8.5.1. kmeans clustering Results 30 8.5.1 Neural network clustering Results 33 9 Analysis of Results 36 10 Future work 37 11 Conclusion 37 12 References 38 Appendix A 40 Appendix B 44 Appendix C 45

  • 6   

    1 Introduction

    1.1 Aim and Scope of the Thesis Advancements in biomedical signal processing techniques have led Electroencephalography

    (EEG) signals to be more widely used in the diagnosis of brain diseases and in the field of Brain

    Computer Interface. With the new understanding of brain function, the advent of powerful low-

    cost computer equipment and by the recognition of needs and potentials of people with

    disabilities, now the BCI research concentrate on developing new augmentative communication

    and control technology for those with severe neuromuscular disorders, such as amyotrophic

    lateral sclerosis, brainstem stroke, and spinal cord injury (Wolpaw et al., 2002). The present-day

    BCIs dete