artifacts removal and feature extraction scheme for steady state visual evoked potential based ...

158 Pages·2013·2.33 MB·English

ARTIFACTS REMOVAL AND FEATURE EXTRACTION
SCHEME FOR STEADY STATE VISUAL EVOKED
POTENTIAL BASED BRAIN COMPUTER INTERFACE
A Thesis
Submitted by

G.SARAVANA KUMAR

For the award of the degree
of
DOCTOR OF PHILOSOPHY
In
Electronics and Communication Engineering

Dr.M.G.R Educational and Research Institute
Dr.M.G.R University
(Declared U/S 3 of the UGC Act, 1956)
Periyar E.V.R. High Road, N.H. 4 Highway,
Maduravoyal, Chennai – 600 095
APRIL 2011
69
ARTIFACTS REMOVAL AND FEATURE EXTRACTION
SCHEME FOR STEADY STATE VISUAL EVOKED
POTENTIAL BASED BRAIN COMPUTER INTERFACE
A Thesis
Submitted by

G.SARAVANA KUMAR

For the award of the degree
of
DOCTOR OF PHILOSOPHY
In
Electronics and Communication Engineering

Dr.M.G.R Educational and Research Institute
Dr.M.G.R University
(Declared U/S 3 of the UGC Act, 1956)
Periyar E.V.R. High Road, N.H. 4 Highway,
Maduravoyal, Chennai – 600 095
APRIL 2011
70
DEPARTMENT OF ELECTRONICS AND COMMUNICATION
ENGINEERING
Dr.M.G.R EDUCATIONAL AND RESEARCH INSTITUTE
CHENNAI – 600 095

CERTIFICATE FROM THE SUPERVISOR

Certified that the thesis entitled “Artifacts Removal and Feature Extraction
Scheme for Steady State Visual Evoked Potential Based Brain Computer Interface”
submitted for the degree of Doctor of Philosophy by Mr. G. Saravana Kumar is the record
of research work carried out by him under my guidance and supervision. Certified that
this work has not formed the basis for the award of any degree, diploma, associate-ship,
fellowship or other titles in this University or any other University or Institution of Higher

Learning.

(Dr. S. Ravi)
Supervisor,
Professor & Head
Department of E.C.E.,
Dr. M. G. R. Educational & Research Institute,
Dr. M. G. R. University.
CHENNAI

71
DECLARATION

I declare that the thesis entitled “Artifacts Removal and Feature Extraction
Scheme for Steady State Visual Evoked Potential Based Brain Computer Interface”
submitted by me for the degree of Doctor of Philosophy is the record carried out by me
the materials which are not the results of my own work have been clearly
acknowledged.

Signature of the Research Scholar

72
ACKNOWLEDGEMENT
I like to express my sincere thanks to Revered founder
Mr. A. C. Shanmugam and President Mr. A. C. S. Arun Kumar, for creating a
conducive environment and providing obligatory infrastructure for development
and implementation of this work.

I express my thanks to Dr. P. Aravindhan, Dean (Research),
Dr. M. G. R University, Chennai, for his enthusiastic support and insightful ideas
for this work to flourish.

I express my sincere thanks to Dr. Uma Rajaram Dean E & T, Dr.M.G.R
University, Chennai, for the amicable support provided to carry out this work.

I extend my genuine thanks with gratitude to my guide Dr. S. Ravi,
Professor and Head, Electronics and Communication Engineering, Dr.M.G.R
University, Maduravoyal, Chennai, TamilNadu, for his subtle and considerate
approach in shaping, guiding and directing me towards effective culmination of
this work.

I thank my parents, my wife, and my son who offered unconditional love
and has stood by me all along. I am grateful to my in-laws, brother and sisters for
their unwavering support.

I am thankful to Master R. Saravana Karthick for protecting me from the
horrendous stress and strain, through his charming graceful smile and larger than
life story narration.

G. Saravana Kumar

73
ABSTRACT
Steady State Visual Evoked Potential (SSVEP) based Brain Computer Interface
(BCI) systems allow individuals with motor disabilities to use their brain signals to
control and communicate with external devices whenever they intend to. These systems
are required to remain inactive during all periods in which users do not intend control
referred as No Control (NC) Commands and to identify the user’s intentional control (IC)
commands. This thesis proposes three schemes related to the design of SSVEP based BCI
systems.
i. Electrode Montage Scheme and EEG signal Recording
ii. Artifact Removal Scheme
iii. Feature Extraction
For the recorded EEG composite signal the frequency ranges corresponds to
stimulus related Visual Evoked Potential (VEP) Components. The resulting Spectrum
provides VEP frequency band detection. Using this identified frequency ranges EEG
artifacts can be reduced.

From the output of a visual stimulation paradigm, i.e., electroencephalogram
(EEG), the proposed scheme localizes the presence of delta, theta, alpha, beta frequency
ranges using parameters such as Spectral Profile (SP) and Peak Power Frequency (PPF).
The time-shift based denoising separation extracts specific features using wavelet
transforms, which demarcate stimulus evoked features from various rhythms of EEG.
This scheme shifts the waveforms by a series of time delays and subsequently linear
combinations are formed by repetitive stimulus presentations. The results demonstrate
that Bipolar montage scheme can be used as effective montage scheme for SSVEP based
BCI. Potential distribution across various regions of the brain for a specific event
represented by single map and tri map supports the effectiveness of feature extraction
scheme. The efficiency of the artifact removal scheme is substantiated through
comparison of two factors True Positive Rate (TPR) and False Positive Rate (FPR) for
SSVEP based BCI systems.

Keywords: Brain Computer Interface (BCI), Electroencephalogram (EEG), Wavelet
Transform (WT), Steady State Visual Evoked Potential (SSVEP), Artifact
Removal
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Table Of Contents i
List of Tables vii
List of Figures ix

Chapter. No. Description Page No.
Chapter 1
1. INTRODUCTION 1
1.1 Problem Statement and Proposed Solution 1
1.2 Focus of Research 3
1.3 Proposed Implementation 4
1.4 Objectives of the Research Work 4
1.5 Benefits of the Research Work 5
1.6 Methodology 6
1.6.1 Experimental Setup 6
1.6.2 Electrode Connection 6
1.6.3 Electrode Placement 7
1.7 Conclusion 8
1.8 Organization of the Thesis 9
1.9 Relevant Works Published
1.9.1 Paper 1 9
1.9.2 Paper 2 10
1.9.3 Paper 3 11
1.9.4 Paper 4 12
Chapter 2
2. LITERATURE SURVEY 14
2.1 Review of Literature Survey on Artifact
14
Removal techniques
2.2 Review of Literature Survey on Brain
18
Computer Interface and Feature Extraction
75
2.3 Review of Literature Survey on Wavelet
24
Transforms
2.4 Review of Literature Survey on Eye movement
28
And tracking methods
2.5 Review of Literature Survey on Visual Evoked
29
Potentials
2.6 Conclusion 31
Chapter 3
3. BRAIN COMPUTER INTERFACE AND
32
WAVELETS
3.1 Need for BCI 32
3.2 Definition and Classification 32
3.3 Non Invasive BCIs 33
3.4 Invasive BCI 33
3.5 Design factors for BCIs 35
3.6 Existing Systems 36
3.7 Brain Response Interface 36
3.8 P3 character recognition 37
3.9 ERS/ERD Cursor Control 37
3.10 SSVEP based BCI 37
3.11 Mu rhythm Cursor Control 38
3.12 Thought Translation Device 38
3.13 An Implanted BCI 38
3.14 Common Signals used in BCIs 38
3.15 Visual Evoked Potential 39
3.15.1 Definitions and Types 40
3.15.2 P3 Component 40
3.15.3 Steady State Visual Evoked Potential
41

3.16 Repetitive Visual Stimuli (RVS) Classification 41
3.16.1 Light Stimuli 41
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3.16.2 Single Visual Stimuli 42
3.16.3 Pattern Reversal Stimuli 42
3.17 Stimulation Type 42
3.17.1 Characteristics of Single graphic stimuli 42
3.17.2 Characteristics of Pattern Reversal stimuli 43
3.18 Comparison between Various Stimuli 44
3.19 Slow Cortical Potentials 44
3.20 Wavelets for Feature Extraction 45
3.20.1 Definition and Characteristics 45
3.20.2 Reasons for Opting Wavelets 46
3.20.3 Numerical Implementation of Wavelet
46
Transforms
3.20.3.1 Continuous Wavelet Transform 46
3.20.3.2 The Mexican Hat Wavelet 47
3.20.3.3 The Morlet Wavelet 47
3.20.3.4 The Discrete Wavelet Transform 48
3.20.3.5 Advantages 48
3.20.4 Implementation Schemes 49
3.20.5 Wavelet mapping to Neuroelectric
51
waveforms
3.20.5.1 Matching Pursuit 51
3.20.5.2 Disadvantages of Matching Pursuit
51
Technique
3.21 Wavelet Denoising Algorithm for Feature
52
Extraction
3.22 Matched Meyer Wavelets 53
3.23 Conclusion 53

Chapter 4
4. ELECTRODES AND EXPERIMENTAL SETUP 54
4.1 Introduction 54
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4.2 Preparation Requirements 54
4.2.1 Scalp Electrodes 55
4.2.2 Subdermal Electrodes 55
4.2.2.1 Usage method 56
4.2.3 Clip Electrode 56
4.2.4 Nasopharyngeal Electrode 57
4.2.4.1 Usage method 57
4.2.5 Sphenoidal Electrode 57
4.2.6 Tympanic Electrode 58
4.2.7 Depth Electrode 58
4.2.7.1 Usage method 58
4.2.8 Cortical Electrode 59
4.2.9 Subdural Electrode 60
4.2.10 Epidural Electrode 60
4.2.11 Electrode Connection 61
4.2.12 Electrode Placement Scheme 63
4.2.13 EEG amplifiers 66
4.2.14 Conclusion 68
Chapter 5
5. WAVELET ANALYSIS OF COMPOSITE EEG
69
SIGNAL
5.1 Introduction 69
5.2 Feature Extraction Using Wavelets 69
5.2.1 Time Series Features 69
5.2.2 Spatial Features 70
5.3 EEG signal model 71
5.4 Disadvantages of Fourier Transform in Feature
71
Extraction
5.5 Energy Spread Calculation 72
5.6 HAAR Expansion System 72
5.7 Relationship between Segments 73
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