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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5276_Библиотеки_им_академика_М_И_Перельмана
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P300 WAVE DETECTION USING A COMMERCIAL
NON-INVASIVE EEG SENSOR: RELIABILITY AND
PERFORMANCE IN CONTROL APPLICATIONS
By
NIZAR ALI KHEMRI
Bachelor of Science in Computer Engineering
Al Fateh University
Tripoli, Libya
2006
Submitted to the Faculty of the
Graduate College of the
Oklahoma State University
in partial fulfillment of
the requirements for
the Degree of
MASTER OF SCIENCE
July, 2012
All rights reserved
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ii
P300 WAVE DETECTION USING A COMMERCIAL
NON-INVASIVE EEG SENSOR: RELIABILITY AND
PERFORMANCE IN CONTROL APPLICATIONS
Thesis Approved:
Dr. Weihua Sheng
Thesis Adviser
Dr. Gary E. Young
Dr. Nazanin Rahnavard
Dr. Sheryl A. Tucker
Dean of the Graduate College
iii
TABLE OF CONTENTS
Chapter Page
1. INTRODUCTION 1
1.1 Motivations ........................................................................................................1
1.2 Objectives ..........................................................................................................3
1.3 Outlines ..............................................................................................................4
2. INTRODUCTION TO BRAIN-COMPUTER INTERFACES 6
2.1 Introduction ........................................................................................................6
2.2 Brain Signals Acquisition ..................................................................................7
2.2.1 Electroencephalography (EEG) ................................................................8
2.2.2 Invasive Methods ....................................................................................10
2.3 Neurophysiological Signals .............................................................................12
2.3.1 Event-Related Potentials (ERPs) ............................................................13
2.3.2 Oscillatory Brain Activity .......................................................................14
2.3.3 Slow Cortical Potentials (SCPs) .............................................................16
2.3.4 Neuronal Ensemble Activity ...................................................................16
2.4 P300-Based Brain-Computer Interfaces ..........................................................16
2.4.1 The P300 Event-Related Potential ..........................................................17
2.4.2 Factors Influencing the P300 ..................................................................19
2.5 P300-Based BCI Systems and Applications ....................................................22
2.5.1 P300 Speller ............................................................................................22
2.5.2 Cursor Control ........................................................................................24
2.5.3 Wheel Chair Control ...............................................................................26
2.5.4 Environment Control ..............................................................................27
2.6 Review of Available Low-Cost EEG Sensors and BCIs .................................27
2.6.1 Overview .................................................................................................27
2.6.2 Emotiv Epoc Headset ..............................................................................30
3. ALGORITHMS FOR P300-BASED BCI SYSTEMS 33
3.1 Introduction ......................................................................................................33
3.2 EEG Raw Data Preprocessing .........................................................................36
3.2.1 Filtering ...................................................................................................37
3.2.2 Winsorization ..........................................................................................37
3.2.3 Blind Source Separation .........................................................................38
iv
3.3 Feature Extraction ............................................................................................44
3.3.1 Time Domain Features ............................................................................45
3.3.2 Frequency Domain Features ...................................................................49
3.4 Classification Algorithms ................................................................................50
3.4.1 Neural Network .......................................................................................50
3.4.2 Support Vector Machines .......................................................................51
3.5 Evaluation of P300-Based BCI Systems..........................................................53
3.6 Other Issues Relevant to P300-based BCI Development ................................56
4. EXPERIMENTAL METHODS 60
4.1 Introduction ......................................................................................................60
4.2 Related Work ...................................................................................................61
4.3 Experimental Setup for Dataset II of BCI Competition III ..............................65
4.3.1 Datasets Description ...............................................................................65
4.3.2 Data Preprocessing and Feature Extraction ............................................67
4.3.3 Machine Learning ...................................................................................70
4.3.4 Process of Characters Prediction in the Test set .....................................72
4.4 Proposed P300-based BCI System Experiments .............................................74
4.4.1 Experimental Setup .................................................................................74
4.4.2 Experimental Schedule ...........................................................................80
5. RESULTS 86
5.1 Introduction ......................................................................................................86
5.2 Results with BCI Competition Data Sets .........................................................86
5.2.1 Full Set of Electrodes (64 Electrodes) ....................................................87
5.2.2 Subsets of Electrodes ..............................................................................88
5.2.3 Discussions ............................................................................................91
5.3 Results of the Proposed P300-based BCI System............................................93
5.3.1 Experiments Using Headset Default Position .........................................93
5.3.2 Experiments Using Headset Reversed Position ......................................94
5.3.3 Discussions .............................................................................................95
6. CONCLUSION AND FUTURE WORK 98
6.1 Summary ..........................................................................................................98
6.1.1 P300-based BCI systems up-to-date approaches and algorithms ...........98
6.1.2 An inexpensive P300-based BCI development.......................................99
6.2 Perspectives and Future Directions ................................................................100
6.2.1 EEG Sensor ...........................................................................................100
6.2.2 Control Applications .............................................................................101
REFERENCES 102
v
LIST OF TABLES
Table Page
2.1 Neurophysiological signals frequency bands....................................................12
2.2 The cheapest EEG acquisition hardware for on-line BCI systems ...................28
3.1 Confusion matrix for evaluation .......................................................................54
5.1 Classification accuracy for each subject using the two approaches with
different electrodes configurations after 5 and 15 trials ..................................91
5.2 System performance for each subject in the default headset mount .................93
5.3 System performance for each subject in the reversed headset mount ..............94
vi
LIST OF FIGURES
Figure Page
1.1 Some commercially available BCI headsets: a) NeuroSky Mindset; b)
Emotiv Epoc .....................................................................................................3
2.1 General block diagram of a BCI system ...........................................................7
2.2 Electrode placement according to the 10-20 international system ...................9
2.3 Typical P300 wave ..........................................................................................18
2.4 Paradigms for evoking the P300: a) Oddball paradigm; b) Three-stimulus
paradigm .........................................................................................................19
2.5 Working principle of the P300 speller: a) Example for a symbol matrix
that can be used in the P300 speller; b) A random stimulus sequence ...........23
2.6 Schematic diagram of the visual stimuli in Kanoh et al., [67] ........................25
2.7 A brain controlled wheelchair: a) A snapshot of a participant navigating
along a corridor; b) Information represented in the visual display ................26
2.8 The display used by Hoffmann et al., [80] for evoking the P300 wave .........27
2.9 Tentative scale for usability score of 8 low-cost EEG acquisition devices ....29
2.10 NeuroFocus Mynd
TM
headset .........................................................................29
2.11 Emotiv Epoc: a) The headset: b) Electrodes positions on the head ................30
2.12 Emotiv SDK control panel ..............................................................................31
3.1 Data Windsorization .......................................................................................37
3.2 The difference between PCA and ICA on a non-orthogonal mixture of
two distributions that are independent and highly sparse (peaked with
long tails): a) PCA; b) ICA .............................................................................40
3.3 FastICA algorithm test example: a) The five original different sources;
three sine waves at different frequencies with some phase shifts with
some sources, square wave and sawtooth wave; b) The sources were
mixed with some mixing matrix; c) The resultant components from
FastICA algorithm ..........................................................................................43
3.4 DWT: a) sub-band coding algorithm of DWT; b) Wavelet function ψ ..........48
3.5 FFMLP NN structure ......................................................................................51
4.1 Stimuli paradigm used by Piccione et al., [28]. ..............................................61
4.2 Stimuli paradigm in Kansaku et al. to control the robot and the desk light ....63
4.3 Stimuli paradigm and experiment setup for Ramírez-Cortes et al. study .......64
4.4 Paradigm and experiment setup for the NeuroPhone application ...................65
4.5 Example of a 6 × 6 user display in P300 Speller ............................................66
vii
Figure Page
4.6 Averaged wave on Cz channel from BCI competition III training data set
for subject A ...................................................................................................67
4.7 Example of FastICA performance with data of a target set: a) Filtered
EEG data; b) FastICA output components; (c) The fourth component of
the ICA output which is similar to P300 wave ...............................................69
4.8 Visual stimuli paradigm and experiment setup of the proposed BCI
system .............................................................................................................75
4.9 Proposed Stimuli Paradigm-1: a) Paradigm-1.a: arrows stimuli; b)
Paradigm-1.b: buttons stimuli ........................................................................76
4.10 Proposed Stimuli Paradigm-2: arrows stimuli in the corners .........................77
4.11 Emotiv headset experimental electrodes placement: a) Electrodes
placement for the default position; b) Approximated chosen electrodes
placement for the reversed position ................................................................78
4.12 Flow chart of EEG raw data acquiring process ..............................................79
5.1 Comparison of the three different classifiers for the first approach as
applied to the full set of electrodes data of both subjects ...............................87
5.2 Comparison of the three different classifiers for the second approach as
applied to the full set of electrodes data of both subjects ...............................87
5.3 Comparison of the three different classifiers for the first approach as
applied to the predefined 8 electrodes data set for both subjects ...................88
5.4 Comparison of the three different classifiers for the second approach as
applied to the predefined 8 electrodes data set for both subjects ...................88
5.5 Comparison of the three different classifiers for the first approach as
applied to the 8 electrodes data set similar to Emotiv default position for
both subjects ...................................................................................................89
5.6 Comparison of the three different classifiers for the second approach as
applied to the 8 electrodes data set similar to Emotiv default position for
both subjects ...................................................................................................89
5.7 Comparison of the three different classifiers for the first approach as
applied to the 8 electrodes data set similar to Emotiv reversed position
for both subjects .............................................................................................90
5.8 Comparison of the three different classifiers for the second approach as
applied to the 8 electrodes data set similar to Emotiv reversed position
for both subjects .............................................................................................90
5.9 Best achieved path ..........................................................................................94
1
CHAPTER 1
INTRODUCTION
This thesis proposes a P300-based BCI system using a recently developed inexpensive
commercial non-invasive EEG sensor. The research work studies and reviews the literature for
the state-of-the-art of P300 wave evoking and detection approaches and algorithms. This research
uses these algorithms and approaches to develop the proposed BCI system. Finally, this thesis
describes results from tests of the reliability and performance of the proposed system in simple
control applications. In this chapter, the motivation and objectives of this thesis are discussed as
an overall introduction. The layout of this thesis is presented in the end of this chapter.
1.1 Motivations
Brain computer interface (BCI) (particularly non-invasive electroencephalography (EEG) based
BCI) research has increased enormously in the past ten years. Knowledge in this multi-
disciplinary field is big, but it is still far from completely being explored. Most of the research
related to a control-centered BCI is based on assistive technologies, allowing severely
handicapped patients to interact with their environment. Research is also done with able-bodied
people to improve their abilities in multi-task control operations. Unfortunately, reasonably
affordable to common individuals, commercially, available BCI systems only target the media
and PC gaming industry.
2
The cost, power and processing abilities of embedded and digital signal processors, as
well as the abilities of BCIs are constantly improving. The level of the technology has reached the
point that some feature extraction and classification algorithms for BCIs can be supported on low
cost embedded systems. This has been proven true by several commercial EEG BCI systems
developers such as NeuroSky and Emotiv with recent commercially available BCI headsets
named Mindset [59] and Epoc [26] respectively. These systems are designed to detect and
classify brain activity for media and games applications.
Despite all of the available technologies mentioned above, a complete and affordable BCI
system that is suitable for different control applications such as home appliances and wheelchairs
does not exist. Until recently, the cost of implementing BCI systems was expensive, including the
much reduced price for the medial BCI systems, and this made them rare. Researchers commonly
use these systems. Patients with severe handicap, such as locked-in syndrome, will occasionally
use these devices if they are supported by funded organizations.
In addition to the cost, a common feature between all of the current types of BCIs
including the expensive medical BCIs is that, because the recorded brain signal is very noisy and
has large diversity, either the ambiguity on executing the command will be high, or the time
between sequential commands will be long. This delay may be in the order of seconds or even
minutes. This is for scientific and medical BCI systems that are designed with high accuracy. One
may ask about sensors that were produced and established for commercial aims such as games
and entertainment. Consequently, the main question is whether the limited number of electrodes
and the quality of consumer priced EEG headset sensors make it feasible to capture brain activity
data in noisy environments. Furthermore, is it possible to use such poor signals to control tasks
that require real-time and accurate control specifications?
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