Brain-Computer Interface. Using Deep Learning Applications

323 Pages·2023·28.089 MB·English

by  M.G. Sumithra, Rajesh Kumar Dhanaraj, Mariofanna Milanova, Balamurugan Balusamy, Chandran Venkatesan

Brain-Computer Interface
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Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Brain-Computer Interface
Using Deep Learning Applications
Edited by
M.G. Sumithra
Rajesh Kumar Dhanaraj
Mariofanna Milanova
Balamurugan Balusamy
and
Chandran Venkatesan
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-85720-4
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Contents
Preface xiii
1 Introduction to Brain–Computer Interface: Applications
and Challenges 1
Jyoti 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 25
Muskan 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
v
vi Contents
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 63
Lalit 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 77
Zahra 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
Contents vii
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
viii Contents
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 157
T. Jagadesh, A. Reethika, B. Jaishankar and M.S. Kanivarshini
7.1 Introduction 158
7.2 Methodology 164
7.3 Experimental Results 169