Detection and removal of eyeblink artifacts from EEG using wavelet analysis and independent

103 Pages·2016·2.06 MB·English

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Jimy Pesin
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Pesin, Jimy, "Detection and removal of eyeblink artifacts from EEG using wavelet analysis and
independent component analysis" (2007). Thesis. Rochester Institute of Technology. Accessed from
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Detection and Removal of Eyeblink Artifacts from EEG using
Wavelet Analysis and Independent Component Analysis
by
Jimy Pesin
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of
Master of Science in Electrical Engineering

Examining Committee:

_________________________________________________
Advisor: Dr. Daniel B. Phillips

_________________________________________________
Member: Dr. Raghuveer M. Rao

_________________________________________________
Member: Dr. Sohail A. Dianat

_________________________________________________
Department Head: Dr. Vincent J. Amuso

Department of Electrical Engineering
Kate Gleason College of Engineering
Rochester Institute of Technology
Rochester, New York
December 2007
Acknowledgements

Partial support for this research was provided by the National Science Foundation under
Award No. BES-0527358.

I would first like to thank my parents who have always been there with me, supporting
me throughout all of my good and bad decisions. You have always been my source of
motivation and encouragement. Everything good that I have or earned is because of you
and the way you are. I love you.

My advisor Dr. Phillips: Your guidance and support have consistently been of great help
to me in achieving my goals within the past year and has given me invaluable experience.
I am honored to have worked with you and am proud to have worked towards your high
standards.

My committee members Dr. Rao and Dr. Dianat: I greatly appreciate the time you have
spent and the feedback you have given me in helping me address the technical problems I
encountered.

Dr. Berg: Your expertise has been of great help to me. Thank you for providing clinical
data to analyze and taking the time out to meet with me, explain and assess my results
throughout the entire process.

i
My professors Dr. Borkholder, Dr. Moon, Dr. Amuso, and Dr. Saber: You have all
brought out my desire to learn and helped me grow tremendously. I will always strive to
achieve the level of experience you all possess. My successes are attributed to all of you
and are reflections of your teaching abilities.

My friends: All of you have somehow made these last five years that much more
enjoyable and special to me. My days were a little brighter with all of you around even in
the Rochester winters. Avery, the competition in absolutely everything has been a
pleasure; you made it fun. Andrea, I am so glad to have known you; you made even the
dreary days that much better with a welcoming hug. Hans, your laughter is infectious and
I appreciate all the help you have given me…good friend. Steve and Tim, you were both
a relief every day. You gave me that well needed break from school that I desperately
needed. Rich, I have always valued your positive attitude and friendliness. Thank you and
your parents for helping in a time of need.

ii
Abstract

Electrical signals generated by brain activity that are measured by the
electroencephalogram can be distorted by electrical activity originating from eyeblinks
and eye movements. This thesis proposes a new technique to identify and remove
eyeblink artifacts from EEG data. An algorithm using a combination of wavelet analysis
and independent component analysis (ICA) is implemented to detect the temporal
location of the eyeblink artifact and eliminate it without compromising the integrity of
the primary EEG data.

The discrete wavelet transform is performed on 10 second epochs of data to detect the
occurrence of ocular artifact. ICA is used to separate out the independent components
within the data and the temporal locations of the eyeblink are used to remove the artifact
and reconstruct the EEG data without that source of distortion. The results obtained
indicate that the technique implemented may be robust enough to effectively process
EEG data and is capable of removing eyeblink artifacts successfully when they are
prominent and the data does not contain a great deal of movement artifact. The results
show an 88.68% detection rate, a false positive rate of 4.03%, and an 87.23% removal
rate for all eyeblinks that were accurately detected. The statistics obtained compared
favorably with work done by others in this field of investigation.

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Table of Contents
Acknowledgements ............................................................................................................ i 
Abstract ............................................................................................................................. iii 
List of Abbreviations ....................................................................................................... vi 
List of Tables ................................................................................................................... vii 
List of Figures ................................................................................................................. viii 
Chapter 1: Introduction ....................................................................................................1 
1.1  Motivation ....................................................................................................................... 1 
1.2  EEG and Ocular Artifact ................................................................................................. 2 
1.3  Thesis Outline .................................................................................................................. 4 
Chapter 2: Methods of Ocular Artifact Removal ...........................................................6 
2.1  Manual Methods of Ocular Artifact Detection and Removal .......................................... 6 
2.2  Linear Filtering ................................................................................................................ 7 
2.3  Regression Analysis ........................................................................................................ 8 
2.4  Principal Component Analysis & Singular Value Decomposition ................................ 10 
Chapter 3: Theoretical Development .............................................................................13 
3.1  Model of Observed Brain Wave Activity ...................................................................... 14 
3.1.1  ICA as a way to separate sources of recorded brain wave activity ............................... 14 
3.1.2  Wavelet Analysis for Detecting Patterns in EEG .......................................................... 15 
3.2  Wavelet Analysis ........................................................................................................... 16 
3.2.1  Theoretical Background ................................................................................................ 16 
3.2.2  Wavelet Packets ............................................................................................................ 21 
3.2.3  Choosing a Wavelet ...................................................................................................... 22 
3.3  Independent Component Analysis ................................................................................. 23 
3.3.1  The Cocktail Party Problem .......................................................................................... 23 
3.3.2  Theoretical Background ................................................................................................ 24 
3.3.3  Independence ................................................................................................................ 26 
3.3.4  Non-Gaussianity ............................................................................................................ 28 
3.3.5  Cost Functions ............................................................................................................... 29 
3.3.6  Contrast Functions ........................................................................................................ 31 
3.3.7  Preprocessing ................................................................................................................ 33 
3.3.8  Orthogonalization .......................................................................................................... 35 
3.3.9  Other Methods ............................................................................................................... 36 
3.4  FastICA .......................................................................................................................... 37 
Chapter 4: Implementation .............................................................................................40 
4.1  The Complete Algorithm Structure ............................................................................... 40 
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4.2  Eyeblink Detection Algorithm ....................................................................................... 42 
4.3  Eyeblink Removal Algorithm ........................................................................................ 45 
4.3.1  Specifications Chosen for FastICA ............................................................................... 49 
4.4  Graphical User Interface (GUI) ..................................................................................... 50 
4.5  Data Set ......................................................................................................................... 51 
Chapter 5: Experimentation and Results ......................................................................52 
5.1  Data Acquisition ............................................................................................................ 53 
5.2  Eyeblink Detection ........................................................................................................ 54 
5.3  Eyeblink Removal ......................................................................................................... 64 
5.4  Summary of Results ....................................................................................................... 75 
Chapter 6: Discussion ......................................................................................................78 
6.1  Successful Results ......................................................................................................... 78 
6.2  Problems Encountered ................................................................................................... 80 
6.3  Experiment Design ........................................................................................................ 83 
Chapter 7: Conclusions and Future Work ....................................................................85 
7.1  Concluding Remarks ..................................................................................................... 85 
7.2  Future Work ................................................................................................................... 85 
7.2.1  Controlled Experimentation .......................................................................................... 86 
7.2.2  Adaptive Threshold ....................................................................................................... 86 
7.2.3  Choosing Eyeblink Components ................................................................................... 86 
7.2.4  Integrating a Sliding Window ....................................................................................... 87 
7.2.5  Saccade Detection and Removal ................................................................................... 87 
7.2.6  Metric for Removal Efficacy ......................................................................................... 88 
7.2.7  Detection and Removal of Other Artifacts .................................................................... 88 
References .........................................................................................................................89 

v
List of Abbreviations

BSS Blind Source Separation
DWT Discrete Wavelet Transform
ECG Electrocardiogram
EEG Electroencephalogram
EOG Electrooculogram
FT Fourier Transform
GUI Graphical User Interface
ICA Independent Component Analysis
PCA Principal Component Analysis
pdf Probability Density Function
STFT Short Time Fourier Transform
SVD Singular Value Decomposition

vi
List of Tables
Table 2.1 – Summary of Ocular Artifact Removal techniques ......................................... 12 
Table 4.1 – Summary of FastICA Specifications ............................................................. 49 
Table 5.1 – Statistics for Eyeblink Data Sets .................................................................... 52 
Table 5.2 – Eyeblink Detection Results for 2 Patients with Minimal Movement ............ 58 
Table 5.3 – Eyeblink Detection Results for 3 Patients with Movement ........................... 59 
Table 5.4 – Detection Results of Data Demonstrating False Positives ............................. 63 
Table 5.5 – Eyeblink Removal Results with Minimal Movement .................................... 70 
Table 5.6 – Eyeblink Removal Results with Movement .................................................. 71 
Table 5.7 – Run Time for Data Sets with and without Movement; dashes indicate a run
time not recorded due to an eyeblink detection count of zero .......................................... 76 
Table 5.8 – Summary of Results Obtained ....................................................................... 77 

vii
List of Figures
Figure 1.1 – (a) EEG contaminated with Eyeblink Artifact (b) EEG contaminated with
Eye Movement Artifacts [15] ............................................................................................. 3 
Figure 1.2 – International 10-20 System for Electrode Placement (Top View) [17] ......... 4 
Figure 2.1 – Normalized Frequency Spectrum of Eyeblink waveforms: Averaged over
500 eyeblinks plotting amplitude (mV) vs. Frequency [7] ................................................. 7 
Figure 3.1 – DWT Decomposition Scheme ...................................................................... 19 
Figure 3.2 - DWT Reconstruction Scheme ....................................................................... 20 
Figure 3.3 – Illustration of approximation (LR – Low Resolution) and four detail sub-
bands [28].......................................................................................................................... 20 
Figure 3.4 – Four Level Wavelet Packet Decomposition [28] ......................................... 22 
Figure 3.5 – Illustration of Cocktail Party Problem [32] .................................................. 24 
Figure 3.6 – Joint distribution of two independent variables with Uniform Distributions;
random variable s on the horizontal axis and random variable s on the vertical axis [33]
1 2
........................................................................................................................................... 27 
Figure 3.7 – Joint distribution of two observed mixtures; mixed random variable x on the
1
horizontal axis and mixed random variable x on the vertical axis [33] ........................... 27 
2
Figure 3.8 – Samples Needed to Estimate the Mixing Matrix of different sizes .............. 35 
Figure 3.9 – Deflationary Orthogonalization Approach Algorithm ................................. 39 
Figure 3.10 – Symmetric Orthogonalization Approach Algorithm .................................. 39 
Figure 4.1 – Flow Chart of the Complete Algorithm........................................................ 41 
Figure 4.2 – Flow Chart of the Eyeblink Detection Algorithm ........................................ 43 
Figure 4.3 – Coiflet3 Wavelet [34] ................................................................................... 44 
Figure 4.4 – Process for characterizing and selecting the Independent Component
associated with the eyeblink artifact ................................................................................. 45 
Figure 4.5 – Flow Chart of the Eyeblink Removal Algorithm ......................................... 48 
Figure 4.6 – Screenshot of GUI used for developed algorithm ........................................ 50 
Figure 5.1 – Frontal Electrode Placement [8] ................................................................... 55 
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