Brain-Computer Interfacing for Assistive Robotics (eBook)

Electroencephalograms, Recurrent Quantum Neural Networks, and User-Centric Graphical Interfaces

Vaibhav Gandhi (Autor)

eBook Download: PDF | EPUB

2014 | 1. Auflage
258 Seiten
Elsevier Science (Verlag)
978-0-12-801587-2 (ISBN)

Infos

Brain-computer interface (BCI) technology provides a means of communication that allows individuals with severely impaired movement to communicate with assistive devices using the electroencephalogram (EEG) or other brain signals. The practicality of a BCI has been possible due to advances in multi-disciplinary areas of research related to cognitive neuroscience, brain-imaging techniques and human-computer interfaces. However, two major challenges remain in making BCI for assistive robotics practical for day-to-day use: the inherent lower bandwidth of BCI, and how to best handle the unknown embedded noise within the raw EEG. Brain-Computer Interfacing for Assistive Robotics is a result of research focusing on these important aspects of BCI for real-time assistive robotic application. It details the fundamental issues related to non-stationary EEG signal processing (filtering) and the need of an alternative approach for the same. Additionally, the book also discusses techniques for overcoming lower bandwidth of BCIs by designing novel use-centric graphical user interfaces. A detailed investigation into both these approaches is discussed. - An innovative reference on the brain-computer interface (BCI) and its utility in computational neuroscience and assistive robotics - Written for mature and early stage researchers, postgraduate and doctoral students, and computational neuroscientists, this book is a novel guide to the fundamentals of quantum mechanics for BCI - Full-colour text that focuses on brain-computer interfacing for real-time assistive robotic application and details the fundamental issues related with signal processing and the need for alternative approaches - A detailed introduction as well as an in-depth analysis of challenges and issues in developing practical brain-computer interfaces.

Vaibhav Gandhi (author) received a First Class (Dist.) degree in Instrumentation & Control engineering in 2000, a First Class (Dist.) Masters degree in Electrical engineering in 2002 and a Ph.D. degree in Computing & Engineering in 2012. He was a recipient of the UK-India Education & Research Initiative (UKIERI) scholarship for his Ph.D. research in the area of Brain-Computer Interface for assistive robotics carried out at the Intelligent Systems Research Center, University of Ulster, UK and partly at IIT Kanpur, India. His Ph.D. focused on quantum mechanics motivated EEG signal processing, and an intelligent adaptive use-centric human-computer interface design for real-time control of a mobile robot for BCI users. His post-doctoral research involved work on shadow-hand multi-fingered mobile robot control using EMG/muscle signals, with contributions in the 3D printing aspects of a robotic hand.He joined the department of Design Engineering & Mathematics, School of Science & Technology, Middlesex University London in 2013, where he is currently Lecturer in Robotics, Embedded Systems and Real-time Systems.His research interests include brain-computer interfaces, biomedical signal processing, computational intelligence and neuroscience, use-centric graphical user interfaces, and assistive robotics.

Inhalt

Front Cover 1
Brain–Computer Interfacing for Assistive Robotics 4
Copyright Page 5
Contents 6
List of Figures 8
List of Tables 12
Preface 14
Acknowledgments 18
List of Acronyms 20
1 Introduction 24
1.1 Introduction 24
1.2 Rationale 27
1.3 Objectives 29
2 Interfacing Brain and Machine 30
2.1 Introduction 30
2.2 The Brain and Electrode Placement 30
2.2.1 EEG Wave Rhythms 32
2.3 Operational Techniques in BCI 34
2.3.1 Synchronous BCI 34
2.3.2 Asynchronous BCI 36
2.4 Data Acquisition 37
2.4.1 The Basics of Data Acquisition 37
2.4.1.1 SMR BCIs 42
2.5 Preprocessing: A Signal Enhancement Requirement along with Noise Reduction 44
2.5.1 Referencing Method 45
2.5.2 Principal Component Analysis [PCA] 46
2.5.3 Independent Component Analysis [ICA] 47
2.5.4 Common Spatial Patterns [CSP] 48
2.5.5 Neural time series prediction preprocessing [NTSPP] 49
2.5.6 Kalman Filter 50
2.5.7 Autoregressive (AR) Modeling 51
2.5.8 Summary 52
2.6 Feature Extraction 52
2.6.1 Band Power Features 52
2.6.2 Power Spectral Density Features 53
2.6.3 Time-frequency Method 54
2.6.4 Hjorth Features 55
2.6.5 Hilbert-Huang Transform 56
2.6.6 Summary 56
2.7 Classification 57
2.7.1 Linear Discriminant Analysis Classifier 57
2.7.2 Support Vector Machine Classifier 59
2.7.3 Regression Classifier 60
2.7.4 Summary 60
2.8 Post-processing 62
2.8.1 Confidence Intervals and Rejection 63
2.8.2 Multiple Thresholding with Windowing Concept 63
2.8.3 De-biasing 64
2.8.4 Error Potential (ErrP) 64
2.9 Validation and Optimization Techniques 65
2.9.1 Cross-validation 65
2.9.2 Genetic Algorithm 66
2.9.3 Particle Swarm Optimization 68
2.10 Graphical User Interface [GUI] 70
2.10.1 The Necessity for an Interface 71
2.10.2 Expectations from a Good GUI Design 75
2.10.3 Recent Developments in BCI GUI Design 76
2.10.3.1 GUI Designs for Speller Applications 76
2.10.3.1.1 P300-based Virtual Keyboard 76
2.10.3.1.2 MI-based Virtual Keyboard 77
2.10.3.1.3 MI-based hex-o-spell Typewriter Interface 78
2.10.3.2 GUI Designs for Robot Control 78
2.10.3.2.1 P300-based GUI with Predefined Fixed Locations 78
2.10.3.2.2 MI-based Robot Control Interface 79
2.11 Strategies in BCI Applications 80
2.11.1 Shared Control BCI System 80
2.12 Performance Measures of a BCI System 82
2.13 Conclusion 85
3 Fundamentals of Recurrent Quantum Neural Networks 88
3.1 Introduction 88
3.2 Postulates of Quantum Mechanics 88
3.3 Quantum Mechanics and the Schrodinger Wave Equation 89
3.3.1 A Classical vs. Quantum Register 92
3.3.2 Quantum Neural Network 93
3.3.2.1 Compelling Motivation Towards the Quantum Filtering Approach 94
3.4 Theoretical Concept of the RQNN Model 96
3.5 Traditional RQNN-Based Signal Enhancement 98
3.5.1 Pseudocode for the RQNN Model 101
3.5.2 RQNN Parameters 101
3.5.3 Filtering Simple Signals 103
3.5.3.1 Method and Performance Analysis 103
3.5.3.2 Concluding Remarks 107
3.6 Revised RQNN-Based Signal Enhancement 107
3.6.1 Pseudocode for the Revised RQNN Model 109
3.6.2 Understanding the Parameters for the Revised RQNN Model 109
3.6.3 Numerical Implementation 110
3.6.4 Filtering Simple Signals 111
3.6.4.1 Method and Performance Analysis 111
3.7 Discussion 113
3.8 Conclusion 116
4 The Proposed Graphical User Interface (GUI) 118
4.1 Introduction 118
4.2 Overview of the Proposed GUI Within the BCI Framework 120
4.2.1 Interface for the Mobility Control Application 123
4.2.1.1 Supervised Mobility Control Interface (Non-Adaptive Form) 123
4.2.1.2 Supervised Mobility Control Interface (Adaptive Form) 125
4.2.1.2.1 The iAUI Architecture 125
4.2.1.2.2 Flowchart and State Machine Diagram 127
4.2.1.2.3 iAUI Operation in an Example Scenario 127
4.2.1.3 Autonomous Mobility Control Interface (MOB) 132
4.2.2 Interface for Arm Control Applications 133
4.3 Interfacing MATLAB and Visual Basic 136
4.4 Conclusion 137
5 Recurrent Quantum Neural Network (RQNN)-Based EEG Enhancement 140
5.1 Introduction 140
5.2 Traditional RQNN Model for EEG Enhancement 143
5.2.1 EEG Filtering without Scaling 143
5.2.2 Scaling the EEG Prior to Filtering 144
5.3 Revised RQNN Model for EEG Signal Enhancement 145
5.3.1 Scaling the EEG Prior to Filtering (with a Large Number of Spatial Neurons) 149
5.3.2 Scaling the EEG Prior to Filtering (Reduced Number of Spatial Neurons) 153
5.4 Towards Subject-Specific RQNN Parameters 163
5.4.1 Two-Step Inner-Outer Five-Fold Cross-Validation for RQNN Parameter Selection 164
5.4.1.1 The Method 164
5.4.1.2 Results and Analysis 167
5.4.1.3 Concluding Remarks 170
5.5 Discussion 170
5.6 Conclusion 172
6 Graphical User Interface (GUI) and Robot Operation 174
6.1 Introduction 174
6.2 The EEG Acquisition Process 175
6.3 RQNN-Based EEG Signal Enhancement 177
6.4 Autonomous and Supervised GUI Operation 179
6.4.1 Maneuvering the Mobile Robot Under a 100% BCI Accuracy Assumption 180
6.4.2 Evaluating the Interface Designs 184
6.4.2.1 Evaluation Quantifiers 184
6.4.2.2 Comparing the Interfaces 190
6.5 Maneuvering the Simulated Mobile Robot Using Only MI EEG 192
6.5.1 Training Paradigm 192
6.5.2 Methodology 193
6.5.3 Results and Discussion 194
6.5.4 Concluding Remarks 202
6.6 Maneuvering the Physical Mobile Robot Using Only MI EEG 203
6.6.1 Methodology 203
6.6.2 Results and Discussion 205
6.6.3 Concluding Remarks 208
6.7 Conclusion 208
7 Conclusion 210
7.1 Contributions of the Book 211
7.1.1 Investigation of QM and SWE for Filter Development 211
7.1.2 Understanding the Parameters of the RQNN Models 212
7.1.3 Tuning/Selecting RQNN Model Parameters 212
7.1.4 Real-Time Implementation of the RQNN Model for EEG Signal Enhancement 212
7.1.5 Investigation into GUIs for Use in BCI Systems 213
7.1.6 Intelligent Adaptive User Interface (iAUI) for Mobility Control 214
7.1.7 Adaptive User Interface for Robot Arm Control 214
7.2 Future Research Directions 215
7.2.1 Tuning/Selecting the RQNN Model Parameters 215
7.2.2 Three-Class Classifier 215
7.2.3 Hybrid BCI Systems 216
7.2.3.1 Hybrid BCI (SSVEP+ERD/ERS) 217
7.2.3.2 Hybrid BCI (EEG+Eye Tracker System) 218
7.3 Conclusion 222
Appendix A: Understanding Evaluation Quantifiers for the Proposed Interface 224
Bibliography 234
Index 254

Textauszug

Chapter 1

Introduction

Verbal or non-verbal information exchange is the basis of human communication. However, some people lose this fundamental ability of communication through accidents or inherited neuromuscular disorders. In the absence of methods for repairing or restoring function due to disease or damage, various alternatives in the form of assistive devices to enable individuals to communicate with and control their environment have been developed. The brain–computer interface (BCI), i.e., electroencephalography (EEG)-based communication, is a new way of controlling devices that does not require eye movement or muscle activity. This chapter introduces the various components of a typical BCI system and explains each component’s importance and function within the complete BCI system.

Keywords

Brain–computer interface; electroencephalography; graphical user interface; motor imagery; signal filtering

1.1 Introduction

Verbal or non-verbal information exchange is the basis of human communication. However, some people lose this fundamental ability of communication because of accidents or inherited neuromuscular disorders. The purpose of the work presented in this book is to contribute to the development of novel methods to allow people to regain freedom of movement/communication by way of controlling devices directly with their brain, bypassing the normal communication channels.

The human brain is estimated to contain about 100 billion neurons [1–4]. The spinal cord acts as an intermediate cable that carries information to and from our brain to control various body parts and their movements. People with an injury to the spinal cord are still able to generate the output signals from the brain, but these signals do not reach the specific body parts because the intermediate spinal cable is damaged. Several technologies using a joystick, head movement, eye gazing and many more may help a physically challenged person to control a robotic device or a wheelchair [5–9]. However, these techniques require the use of partial movement control through the hand, head or eyes etc., and therefore make the control issue less complicated. The issue becomes more challenging when people with complete loss of control over their voluntary muscles are involved, a condition generally known as locked-in syndrome [10,11], in which people are unable to speak and move but are conscious and can think and reason. A number of neurological diseases such as stroke1, severe cerebral palsy2, motor neuron disease (MND)3, amyotrophic lateral sclerosis (ALS), and encephalitis4 can result in such severe motor paralysis [12]. Many of these diseases can lead to restrictions in communication capacity. A brain–computer interface (BCI) can enable such physically challenged people to achieve greater independence by making technology accessible. BCI technology provides an alternative communication channel between the human brain (that does not depend on the brain’s normal output channels of the peripheral nerves and muscles) and a computer [13–21]. The three most commonly discussed diseases/injuries cited in the BCI literature as being a case of locked-in syndrome are ALS, high spinal cord injury and brain stem stroke [16,22–24].

• Patients suffering from ALS can undergo severe physical impairment due to the degeneration of nerve cells that control the voluntary muscles. In the later stages of ALS, the most basic human actions are affected, including speech, swallowing and breathing [25].

• Spinal cord injury (SCI) can result in damage to myelinated fiber tracts or the nerve roots that carry the signals to and from the brain [25]. In complete SCI, most of the motor functions and sensation below the neurological level are affected or completely lost [26]. SCI has a global annual incidence of 15–40 cases per million population [27] and less than 5% of people suffering from SCI recover locomotion [26].

• Brain stem stroke can be fatal, as the brain stem controls many of the basic and fundamental activities for life, such as breathing, heart rate, blood pressure, swallowing and eye movement [28]. People with severe brain stem stroke may also enter into a locked-in state and lose motor functions [29].

BCI (i.e., electroencephalography [EEG])-based communication produces new channels for controlling devices which would not be possible through the modes of communication that require eye movement or some muscle activity. Hans Berger performed a systematic study of the electrical activity of the human brain, and developed the EEG5. The first scientific literature referring to communication between the brain and the computer dates back to the early 1970s, and is due to Vidal [18], who suggested the feasibility of direct brain communication. To achieve this, the intent of the user must be extracted from the brain via the EEG or brain waves.

A typical BCI scheme generally consists of a data acquisition system, preprocessing of the acquired signals, the feature extraction process (FEP), classification of the features and finally the control interface and device controller, as shown in Figure 1.1. The EEG signals are acquired by mounting electrodes on the scalp of the user. These raw EEG signals have very low amplitude [30], very low signal-to-noise (SNR) ratio and considerable noise contamination. Preprocessing is carried out to obtain cleaner EEG signals by removing the unwanted components embedded in the EEG, which can considerably reduce the computational load on the rest of the BCI components.

Figure 1.1 Basic functional block diagram of a simple BCI system.

The work presented in this book has focused on the preprocessing stage for signal enhancement and extracting more motor imagery (MI)6 (mental imagination of movement) [31] related information from the acquired noisy EEG. These raw EEG signals are considered as a realization of a random or stochastic process [32]. When an accurate description of the signal is not available, a stochastic filter can be designed based on probabilistic measures. Therefore, the approach undertaken in this book is to use the concepts from quantum mechanics (QM) and the Schrodinger wave equation (SWE). A recurrent quantum neural network (RQNN) is constructed by using a layer of neurons within the neural network framework by computing a time-varying probability density function (pdf) of the noisy input signal (cf. Chapter 3). This pdf evolves recurrently under the influence of the SWE and helps to enhance the EEG (cf. Chapter 5).

Features are extracted from the filtered EEG. The classifier interprets these extracted features to categorize the input signal into a designated output class. The control interface or the graphical user interface (GUI) further interprets the classifier output in the form of a command to be sent to the controlled device. The GUI also provides appropriate feedback information to the BCI user [7], and can quicken the issuance of the command from the BCI user to the device that is controlled. A two-class BCI system has two output classes in the form of a left hand MI or a right hand/foot MI. But the task of maneuvering a mobile robot requires commands in the form of forward, left, right, backward and start/stop, using just the two-class information; i.e., there is very limited communication bandwidth. However, given the inherent higher accuracy compared to multi-class BCIs, this book focuses on utilizing a two-class BCI. This book proposes an intelligent, adaptive and user-centric interface design that plays a major role in compensating for the low bandwidth of a two-class BCI and simultaneously capitalizes on the intrinsic higher accuracy characteristic that is typical of a two-class BCI system (cf. Chapter 4 and Chapter 6).

In summary, this book outlines the recent developments in MI-based BCI, specifically focusing on reviewing the existing signal processing and classification methodologies, as well as different interface designs for a BCI system. It proposes an alternative nature-inspired information processing approach based on the concepts from QM, which is referred to as the RQNN model and is utilized for EEG signal enhancement (cf. Chapter 3 and Chapter 5). It also proposes an intelligent user interface design (cf. Chapter 4) which is customized to provide effective control of a wheelchair/mobile robot and a robot arm for a quicker communication process (cf. Chapter 6).

1.2 Rationale

BCI technology has not yet reached a critical level of acceptance even forty years after its inception. The challenges in this domain begin right at the stage of acquiring the EEG signals from the brain. An EEG is recorded non-invasively, so it is a mixture of the signal of interest from the activity of the underlying neural networks and an unknown amount of noise. Therefore, the raw signals need to be filtered in order to obtain cleaner EEG signals. Several groups work in the field of EEG filtering [19,33–39]. Most of their approaches involve subject-specific parameters, which, if tuned properly, can enhance the performance of an individual subject in terms of the classification accuracy (CA) [35,40]. However, these frequency-selective techniques lead to an unknown amount of loss of information from the...

Produkt-Details

• Erscheint lt. Verlag: 24.9.2014
• Sprache: englisch
• Themenwelt: Informatik ► Software Entwicklung ► User Interfaces (HCI)
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Medizin / Pharmazie ► Allgemeines / Lexika
• Themenwelt: Medizin / Pharmazie ► Medizinische Fachgebiete ► Neurologie
• Themenwelt: Naturwissenschaften ► Biologie ► Humanbiologie
• Themenwelt: Naturwissenschaften ► Biologie ► Zoologie
• ISBN-10: 0-12-801587-X / 012801587X
• ISBN-13: 978-0-12-801587-2 / 9780128015872