Brain-Computer Interface

Using Deep Learning Applications
Herausgegeben:Sumithra, M. G.; Dhanaraj, Rajesh Kumar; Milanova, Mariofanna; Balusamy, Balamurugan; Venkatesan, Chandran

• Gebundenes Buch

Produktbeschreibung

BRAIN-COMPUTER INTERFACE

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.

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Produktdetails

• Verlag: Wiley & Sons / Wiley-Scrivener
• Artikelnr. des Verlages: 1W119857200
• 1. Auflage
• Seitenzahl: 320
• Erscheinungstermin: 14. März 2023
• Englisch
• Abmessung: 236mm x 158mm x 22mm
• Gewicht: 531g
• ISBN-13: 9781119857204
• ISBN-10: 1119857201
• Artikelnr.: 63123008

Autorenporträt

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.

Inhaltsangabe

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

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

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

Referenc