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It covers all the research prospects and recent advancements in the brain-computer interface using deep learning.
The brain-computer interface (BCI) is an emerging technology that is developing to be more functional in practice. The aim is to establish, through experiences with electronic devices, a communication channel bridging the human neural networks within the brain to the external world. For example, creating communication or control applications for locked-in patients who have no control over their bodies will be one such use. Recently, from communication to marketing, recovery, care, mental state monitoring, and entertainment, the possible application areas have been expanding. Machine learning algorithms have advanced BCI technology in the last few decades, and in the sense of classification accuracy, performance standards have been greatly improved. For BCI to be effective in the real world, however, some problems remain to be solved.
Research focusing on deep learning is anticipated to bring solutions in this regard. Deep learning has been applied in various fields such as computer vision and natural language processing, along with BCI growth, outperforming conventional approaches to machine learning. As a result, a significant number of researchers have shown interest in deep learning in engineering, technology, and other industries; convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN).
Audience
Researchers and industrialists working in brain-computer interface, deep learning, machine learning, medical image processing, data scientists and analysts, machine learning engineers, electrical engineering, and information technologists.
M. G. Sumithra, PhD, is a professor at Anna University Chennai, India. With 25 years of teaching experience, she has published more than 70 technical papers in refereed journals, 3 book chapters, and 130 research papers in national and international conferences. She is a Nvidia Deep Learning Institute Certified Instructor for "Computer Vision".
Rajesh Kumar Dhanaraj, PhD, is a professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed around 25 authored and edited books on various technologies, 17 patents, and more than 40 articles and papers in various refereed journals and international conferences. He is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).
Mariofanna Milanova, PhD, is a professor in the Department of Computer Science at the University of Arkansas, Little Rock, USA. She is an IEEE Senior Member and Nvidia's Deep Learning Institute University Ambassador. She has published more than 120 publications, more than 53 journal papers, 35 book chapters, and numerous conference papers. She also has two patents.
Balamurugan Balusamy, PhD, is a professor in the School of Computing Science and Engineering, Galgotias University, Greater Noida, India. He is a Pioneer Researcher in the areas of big data and IoT and has published more than 70 articles in various top international journals.
V. Chandran holds an M.E degree in VLSI Design from Government College of Technology, Coimbatore, and is a Nvidia Certified Instructor for Deep learning for Computer Vision.
Preface xiii
1 Introduction to Brain-Computer Interface: Applications and Challenges 1Jyoti R. Munavalli, Priya R. Sankpal, Sumathi A. and Jayashree M. Oli
1.1 Introduction 1
1.2 The Brain - Its Functions 3
1.3 BCI Technology 3
1.3.1 Signal Acquisition 5
1.3.1.1 Invasive Methods 6
1.3.1.2 Non-Invasive Methods 8
1.3.2 Feature Extraction 10
1.3.3 Classification 11
1.3.3.1 Types of Classifiers 12
1.4 Applications of BCI 13
1.5 Challenges Faced During Implementation of BCI 17
References 21
2 Introduction: Brain-Computer Interface and Deep Learning 25Muskan Jindal, Eshan Bajal and Areeba Kazim
2.1 Introduction 26
2.1.1 Current Stance of P300 BCI 28
2.2 Brain-Computer Interface Cycle 29
2.3 Classification of Techniques Used for Brain-Computer Interface 38
2.3.1 Application in Mental Health 38
2.3.2 Application in Motor-Imagery 38
2.3.3 Application in Sleep Analysis 39
2.3.4 Application in Emotion Analysis 39
2.3.5 Hybrid Methodologies 40
2.3.6 Recent Notable Advancements 41
2.4 Case Study: A Hybrid EEG-fNIRS BCI 46
2.5 Conclusion, Open Issues and Future Endeavors 47
References 49
3 Statistical Learning for Brain-Computer Interface 63Lalit Kumar Gangwar, Ankit, John A. and Rajesh E.
3.1 Introduction 64
3.1.1 Various Techniques to BCI 64
3.1.1.1 Non-Invasive 64
3.1.1.2 Semi-Invasive 65
3.1.1.3 Invasive 67
3.2 Machine Learning Techniques to BCI 67
3.2.1 Support Vector Machine (SVM) 69
3.2.2 Neural Networks 69
3.3 Deep Learning Techniques Used in BCI 70
3.3.1 Convolutional Neural Network Model (CNN) 72
3.3.2 Generative DL Models 73
3.4 Future Direction 73
3.5 Conclusion 74
References 75
4 The Impact of Brain-Computer Interface on Lifestyle of Elderly People 77Zahra Alidousti Shahraki and Mohsen Aghabozorgi Nafchi
4.1 Introduction 78
4.2 Diagnosing Diseases 79
4.3 Movement Control 84
4.4 IoT 85
4.5 Cognitive Science 86
4.6 Olfactory System 88
4.7 Brain-to-Brain (B2B) Communication Systems 89
4.8 Hearing 90
4.9 Diabetes 91
4.10 Urinary Incontinence 92
4.11 Conclusion 93
References 93
5 A Review of Innovation to Human Augmentation in Brain-Machine Interface - Potential, Limitation, and Incorporation of AI 101 T. Graceshalini, S. Rathnamala and M. Prabhanantha Kumar
5.1 Introduction 102
5.2 Technologies in Neuroscience for Recording and Influencing Brain Activity 103
5.2.1 Brain Activity Recording Technologies 104
5.2.1.1 A Non-Invasive Recording Methodology 104
5.2.1.2 An Invasive Recording Methodology 104
5.3 Neuroscience Technology Applications for Human Augmentation 106
5.3.1 Need for BMI 106
5.3.1.1 Need of BMI Individuals for Re-Establishing the Control and Communication of Motor 107
5.3.1.2 Brain-Computer Interface Noninvasive Research at Wadsworth Center 107
5.3.1.3 An Interface of Berlin Brain-Computer: Machine Learning-Dependent of User-Specific Brain States Detection 107
5.4 History of BMI 108
5.5 BMI Interpretation of Machine Learning Integration 111
5.6 Beyond Current Existing Methodologies: Nanomachine Learning BMI Supported 116
5.7 Challenges and Open Issues 119
5.8 Conclusion 120
References 121
6 Resting-State fMRI: Large Data Analysis in Neuroimaging 127 M. Menagadevi , S. Mangai, S. Sudha and D. Thiyagarajan
6.1 Introduction 128
6.1.1 Principles of Functional Magnetic Resonance Imaging (fMRI) 128
6.1.2 Resting State fMRI (rsfMRI) for Neuroimaging 128
6.1.3 The Measurement of Fully Connected and Construction of Default Mode Network (DMN) 129
6.2 Brain Connectivity 129
6.2.1 Anatomical Connectivity 129
6.2.2 Functional Connectivity 130
6.3 Better Image Availability 130
6.3.1 Large Data Analysis in Neuroimaging 131
6.3.2 Big Data rfMRI Challenges 133
6.3.3 Large rfMRI Data Software Packages 134
6.4 Informatics Infrastructure and Analytical Analysis 137
6.5 Need of Resting-State MRI 137
6.5.1 Cerebral Energetics 137
6.5.2 Signal to Noise Ratio (SNR) 137
6.5.3 Multi-Purpose Data Sets 138
6.5.4 Expanded Patient Populations 138
6.5.5 Reliability 138
6.6 Technical Development 138
6.7 rsfMRI Clinical Applications 139
6.7.1 Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) 139
6.7.2 Fronto-Temporal Dementia (FTD) 140
6.7.3 Multiple Sclerosis (MS) 141
6.7.4 Amyotrophic Lateral Sclerosis (ALS) and Depression 143
6.7.5 Bipolar 144
6.7.6 Schizophrenia 145
6.7.7 Attention Deficit Hyperactivity Disorder (ADHD) 147
6.7.8 Multiple System Atrophy (MSA) 147
6.7.9 Epilepsy/Seizures 147
6.7.10 Pediatric Applications 149
6.8 Resting-State Functional Imaging of Neonatal Brain Image 149
6.9 Different Groups in Brain Disease 151
6.10 Learning Algorithms for Analyzing rsfMRI 151
6.11 Conclusion and Future Directions 154
References 154
7 Early Prediction of Epileptic Seizure Using Deep Learning Algorithm 157T. Jagadesh, A. Reethika, B. Jaishankar and M.S. Kanivarshini
7.1 Introduction 158
7.2 Methodology 164
7.3 Experimental Results 169
7.4 Taking Care of Children with Seizure Disorders 172
7.5 Ketogenic Diet 172
7.6 Vagus Nerve Stimulation (VNS) 172
7.7 Brain Surgeries 173
7.8 Conclusion 173
References 175
8 Brain-Computer Interface-Based Real-Time Movement of Upper Limb Prostheses Topic: Improving the Quality of the Elderly with Brain-Computer Interface 179S. Vairaprakash and S. Rajagopal
8.1 Introduction 180
8.1.1 Motor Imagery Signal Decoding 181
8.2 Literature Survey 182
8.3 Methodology of Proposed Work 184
8.3.1 Proposed Control Scheme 185
8.3.2 One Versus All Adaptive Neural Type- 2 Fuzzy Inference System (OVAANT2FIS) 187
8.3.3 Position Control of Robot Arm Using Hybrid BCI for Rehabilitation Purpose 187
8.3.4 Jaco Robot Arm 189
8.3.5 Scheme 1: Random Order Positional Control 189
8.4 Experiments and Data Processing 192
8.4.1 Feature Extraction 195
8.4.2 Performance Analysis of the Detectors 197
8.4.3 Performance of the Real Time Robot Arm Controllers 198
8.5 Discussion 200
8.6 Conclusion and Future Research Directions 202
References 203
9 Brain-Computer Interface-Assisted Automated Wheelchair Control Management-Cerebro: A BCI Application 205 Sudhendra Kambhamettu, Meenalosini Vimal Cruz, Anitha S., Sibi Chakkaravarthy S. and K. Nandeesh Kumar
9.1 Introduction 206
9.1.1 What is a BCI? 207
9.2 How Do BCI's Work? 207
9.2.1 Measuring Brain Activity 208
9.2.1.1 Without Surgery 208
9.2.1.2 With Surgery 208
9.2.2 Mental Strategies 209
9.2.2.1 Ssvep 210
9.2.2.2 Neural Motor Imagery 210
9.3 Data Collection 211
9.3.1 Overview of the Data 211
9.3.2 EEG Headset 213
9.3.3 EEG Signal Collection 214
9.4 Data Pre-Processing 215
9.4.1 Artifact Removal 216
9.4.2 Signal Processing and Dimensionality Reduction 217
9.4.3 Feature Extraction 217
9.5 Classification 218
9.5.1 Deep Learning (DL) Model Pipeline 219
9.5.2 Architecture of the DL Model 220
9.5.3 Output Metrics of the Classifier 221
9.5.4 Deployment of DL Model 221
9.5.5 Control System 223
9.5.6 Control Flow Overview 223
9.6 Control Modes 223
9.6.1 Speech Mode 223
9.6.2 Blink Stimulus Mapping 223
9.6.3 Text Interface 225
9.6.4 Motion Mode 225
9.6.5 Motor Arrangement 225
9.6.6 Imagined Motion Mapping 226
9.7 Compilation of All Systems 226
9.8 Conclusion 226
References 227
10 Identification of Imagined Bengali Vowels from EEG Signals Using Activity Map and Convolutional Neural Network 231Rajdeep Ghosh, Nidul Sinha and Souvik Phadikar
10.1 Introduction 232
10.1.1 Electroencephalography (EEG) 233
10.1.2 Imagined Speech or Silent Speech 233
10.2 Literature Survey 234
10.3 Theoretical Background 238
10.3.1 Convolutional Neural Network 238
10.3.2 Activity Map 240
10.4 Methodology 242
10.4.1 Data Collection 243
10.4.2 Pre-Processing 244
10.4.3 Feature Extraction 245
10.4.4 Classification 247
10.5 Results 249
10.6 Conclusion 252
Acknowledgment 252
References 252
11 Optimized Feature Selection Techniques for Classifying Electrocorticography Signals 255B. Paulchamy, R. Uma Maheshwari, D. Sudarvizhi AP(Sr. G), R. Anandkumar AP(Sr. G) and Ravi G.
11.1 Introduction 256
11.1.1 Brain-Computer Interface 256
11.2 Literature Study 258
11.3 Proposed Methodology 260
11.3.1 Dataset 261
11.3.2 Feature Extraction Using Auto-Regressive (AR) Model and Wavelet Transform 261
11.3.2.1 Auto-Regressive Features 261
11.3.2.2 Wavelet Features 262
11.3.2.3 Feature Selection Methods 262
11.3.2.4 Information Gain (IG) 263
11.3.2.5 Clonal Selection 263
11.3.2.6 An Overview of the Steps of the Clonalg 264
11.3.3 Hybrid CLONALG 265
11.4 Experimental Results 268
11.4.1 Results of Feature Selection Using IG with Various Classifiers 272
11.4.2 Results of Optimizing Support Vector Machine Using CLONALG Selection 274
11.5 Conclusion 276
References 277
12 BCI - Challenges, Applications, and Advancements 279R. Remya and Sumithra, M.G.
12.1 Introduction 279
12.1.1 BCI Structure 280
12.2 Related Works 281
12.3 Applications 282
12.4 Challenges and Advancements 297
12.5 Conclusion 299
References 299
Index 303
Jyoti R. Munavalli1*, Priya R. Sankpal1, Sumathi A.1 and Jayashree M. Oli2
1ECE, BNM Institute of Technology, Bangalore, India
2Amrita School of Engineering, ECE, Bengaluru, Amrita Vishwa Vidyapeetham, India
Brain-Computer Interface (BCI) is a technology that facilitates the communication between the brain and the machine. It is a promising field that has lot of potential to be tapped for various applications. To begin with, this chapter explains the basics of the brain and its function. It describes the BCI technology and the steps: from signal acquisition to applications. The signal capturing is done through invasive and non-invasive methods. The features from the brain signals are extracted and classified using various advanced machine learning classification algorithms. BCI is extensively helpful for health-related problems but it also has applications in education, smart homes, security and many more. BCI has its own share of challenges that it has to overcome so that it could be beneficial in the future use. We discuss about all the issues like ethical, technical and legal. This chapter provides an overview on BCI through basics, applications, and challenges.
Keywords: Brain-Computer Interface, BCI technology, BCI applications, BCI challenges
In the past 20 years, the world has seen tremendous changes in the technology. Many technologies were invented that really affected the society for/in their well-being. We are witnessing new arenas like Artificial Intelligence, Virtual Reality, electronic health records, robotics, Data Science, and many more. All these have revolutionized the healthcare delivery system. Artificial Intelligence has paved its way in diagnosis, prediction of diseases through its advanced algorithms like machine learning and deep learning [1]. Virtual reality assists in treatment plans like phobias and neurological disorders [2]. EMR-based real time optimization has improved the efficiency of hospital systems and aid in decision making, again through technological intervention [3-7]. It has been observed that robotic assisted surgeries and the extent to which data science was utilized during pandemic are the big marking of technology in healthcare (Healthcare 4.0). With these technological interventions, Brain Computing Interface (BCI) is one among them.
In 1920, the first record to measure brain activity of human was by means of EEG but the device was very elementary. Later in 1970, research on BCI that was particularly for neuro-prosthetic, began at the University of California, Los Angeles, but it was in 1990s that these devices were actually implemented in humans.
A Brain-Computer Interface is also referred as Brain Machine Interface or Mind-Machine Interface. BCI is a computer-based system that acquires the signals based on the activities in the brain and analyzes and translates the neuronal information into commands that can control external environment (either hardware or software). It is an Artificial Intelligence system that identifies the patterns from the collected brain signals. The electrical signals that are generated during brain activities are used in interaction or change with the surroundings. It allows individuals that are not capable to talk and/or make use of their limbs for operating the assistive devices that help them in walking and handling and controlling the objects [8]. BCI is extensively used in Medicine and Healthcare [9].
This chapter presents the overview of BCI: its history and basics, the process details with hardware components, its applications and then finally the challenges faced while dealing with BCI. We begin with the description of functional areas of brain.
Figure 1.1 Brain parts.
The brain is a soft mass made up of the nerves and tissues that are connected to the spinal cord. The main parts of the brain are Cerebrum, Cerebellum and Brain stem (see Figure 1.1). Frontal lobe, temporal lobe, parietal lobe and occipital lobe, are the four lobes of cerebrum. They are responsible for reading, learning, thinking, emotions, walking, vision, and hearing (regarding senses). Cerebellum is responsible for balancing and coordination. Brain stem is responsible for heartbeat, breathing, blood pressure, swallowing, and eye movements [10, 11].
Brain generates many signals and the electrical signals generated are used in BCI system. These signals are measured using invasive or non-invasive techniques.
BCI as mentioned earlier is a communication channel between the brain and the external processing device. The goal of BCI technology is to give a communication model to those people who are severely paralyzed and do not have control over their muscles [12]. It takes the bio-signals measured from a person and predicts some abstract facet of cognitive state.
Most commonly, the BCI focuses on patients that have problems with motor state and cognitive state. In normal humans, there is an intersection of brain activity, eye movement, and body movements. If any one of them is missed, it results in constrained state. Figure 1.2 shows this intersection. It is observed that BCI is applicable to the areas where patients have normal to major cognition levels working along with no motor state response to minor motor state response. So under this umbrella, we get patients that experience completely locked-in syndrome (CLIS) or Locked-in Syndrome (LiS) [13].
Figure 1.2 BCI domain.
Figure 1.3 Block diagram of BCI.
Locked-in syndrome is a neurological disorder also known as pseudo coma where patient is completely paralyzed that is losing control of voluntary muscles, except the eye movements. Therefore, such people can think and analyze but not speak and move. In recent past, it is seen that chronic LIS can be unlocked with the aid of BCI [14].
The block diagram of BCI is as in Figure 1.3. It begins with recording of signals from brain, then processing of these recorded signals. Here various features from the signals are extracted and classified as per their properties or characteristics. Based on these signals' commands are generated and the BCI device works accordingly.
In BCI, signal acquisitor plays an important role. There are different recording techniques in BCI and are broadly classified as invasive and non-invasive methods as shown in the Figure 1.4. These methods aid to bring out/pull out electric and magnetic signals of brain activity.
Figure 1.4 Types of BCI signal acquisitor.
Electrodes are implanted in the scalp to extract the required parameters and in non-invasive method, external sensors are used to measure the parameters.
a. Intra-Cortical Recording:
A single electrode or sometimes array of electrodes are in the cortex of the brain. These interfaces are been used for the past 70 years and some of the popular kinds of hardware for intracortical recording are as follows:
i. Wire-Based Arrays
They are also called Microwire arrays, Wire arrays are made up of insulated metal wires with an uninsulated tip that is used to observe the bipotential form of neurons in a bipolar environment [15]. The diameter of those wires is in the range of 10-200 micrometers the limitations of microwire-based arrays are as follows:
ii. Micro-Machined Micro-Electrodes
The introduction of photolithography and subsequent advancements in micromachining technology prompted the development of a new generation of silicon-based brain probes. (micromachined microelectrodes) [17]. Ex: Michigan Planar electrode arrays, Utah Electrode arrays [18].
The limitations are as follows:
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