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Submit a ProposalBrain-Computer Interface
 | Using Deep Learning Applications Edited by M.G. Sumithra, Rajesh Kumar Dhanaraj, Mariofanna Milanova, Balamurugan Balusamy and Chandran Venkatesan Copyright: 2023 | Status: Published ISBN: 9781119857204 | Hardcover | 306 pages | 91 illustrations Price: $195 USD  |
One Line Description
It covers all the research prospects and recent advancements in the brain-computer interface using deep learning.
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.
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.
Description
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).
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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).
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Author / Editor Details
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”.
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).
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.
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.
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Table of Contents
Preface
1. Introduction to Brain–Computer Interface: Applications and Challenges
Jyoti R. Munavalli, Priya R. Sankpal, Sumathi A. and Jayashree M. Oli
1.1 Introduction
1.2 The Brain – Its Functions
1.3 BCI Technology
1.3.1 Signal Acquisition
1.3.1.1 Invasive Methods
1.3.1.2 Non-Invasive Methods
1.3.2 Feature Extraction
1.3.3 Classification
1.3.3.1 Types of Classifiers
1.4 Applications of BCI
1.5 Challenges Faced During Implementation of BCI
References
2. Introduction: Brain–Computer Interface and Deep Learning
Muskan Jindal, Eshan Bajal and Areeba Kazim
2.1 Introduction
2.1.1 Current Stance of P300 BCI
2.2 Brain–Computer Interface Cycle
2.3 Classification of Techniques Used for Brain–Computer Interface
2.3.1 Application in Mental Health
2.3.2 Application in Motor-Imagery
2.3.3 Application in Sleep Analysis
2.3.4 Application in Emotion Analysis
2.3.5 Hybrid Methodologies
2.3.6 Recent Notable Advancements
2.4 Case Study: A Hybrid EEG-fNIRS BCI
2.5 Conclusion, Open Issues and Future Endeavors
References
3. Statistical Learning for Brain–Computer Interface
Lalit Kumar Gangwar, Ankit, John A. and Rajesh E.
3.1 Introduction
3.1.1 Various Techniques to BCI
3.1.1.1 Non-Invasive
3.1.1.2 Semi-Invasive
3.1.1.3 Invasive
3.2 Machine Learning Techniques to BCI
3.2.1 Support Vector Machine (SVM)
3.2.2 Neural Networks
3.3 Deep Learning Techniques Used in BCI
3.3.1 Convolutional Neural Network Model (CNN)
3.3.2 Generative DL Models
3.4 Future Direction
3.5 Conclusion
References
4. The Impact of Brain–Computer Interface on Lifestyle of Elderly People
Zahra Alidousti Shahraki and Mohsen Aghabozorgi Nafchi
4.1 Introduction
4.2 Diagnosing Diseases
4.3 Movement Control
4.4 IoT
4.5 Cognitive Science
4.6 Olfactory System
4.7 Brain-to-Brain (B2B) Communication Systems
4.8 Hearing
4.9 Diabetes
4.10 Urinary Incontinence
4.11 Conclusion
References
5. A Review of Innovation to Human Augmentation in Brain-Machine Interface – Potential, Limitation, and Incorporation of AI
T. Graceshalini, S. Rathnamala and M. Prabhanantha Kumar
5.1 Introduction
5.2 Technologies in Neuroscience for Recording and Influencing Brain Activity
5.2.1 Brain Activity Recording Technologies
5.2.1.1 A Non-Invasive Recording Methodology
5.2.1.2 An Invasive Recording Methodology
5.3 Neuroscience Technology Applications for Human Augmentation
5.3.1 Need for BMI
5.3.1.1 Need of BMI Individuals for Re-Establishing the Control and Communication of Motor
5.3.1.2 Brain-Computer Interface Noninvasive Research at Wadsworth Center
5.3.1.3 An Interface of Berlin Brain-Computer: Machine Learning-Dependent of User-Specific Brain States Detection
5.4 History of BMI
5.5 BMI Interpretation of Machine Learning Integration
5.6 Beyond Current Existing Methodologies: Nanomachine Learning BMI Supported
5.7 Challenges and Open Issues
5.8 Conclusion
References
6. Resting-State fMRI: Large Data Analysis in Neuroimaging
M. Menagadevi , S. Mangai, S. Sudha and D. Thiyagarajan
6.1 Introduction
6.1.1 Principles of Functional Magnetic Resonance Imaging (fMRI)
6.1.2 Resting State fMRI (rsfMRI) for Neuroimaging
6.1.3 The Measurement of Fully Connected and Construction of Default Mode Network (DMN)
6.2 Brain Connectivity
6.2.1 Anatomical Connectivity
6.2.2 Functional Connectivity
6.3 Better Image Availability
6.3.1 Large Data Analysis in Neuroimaging
6.3.2 Big Data rfMRI Challenges
6.3.3 Large rfMRI Data Software Packages
6.4 Informatics Infrastructure and Analytical Analysis
6.5 Need of Resting-State MRI
6.5.1 Cerebral Energetics
6.5.2 Signal to Noise Ratio (SNR)
6.5.3 Multi-Purpose Data Sets
6.5.4 Expanded Patient Populations
6.5.5 Reliability
6.6 Technical Development
6.7 rsfMRI Clinical Applications
6.7.1 Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD)
6.7.2 Fronto-Temporal Dementia (FTD)
6.7.3 Multiple Sclerosis (MS)
6.7.4 Amyotrophic Lateral Sclerosis (ALS) and Depression
6.7.5 Bipolar
6.7.6 Schizophrenia
6.7.7 Attention Deficit Hyperactivity Disorder (ADHD)
6.7.8 Multiple System Atrophy (MSA)
6.7.9 Epilepsy/Seizures
6.7.10 Pediatric Applications
6.8 Resting-State Functional Imaging of Neonatal Brain Image
6.9 Different Groups in Brain Disease
6.10 Learning Algorithms for Analyzing rsfMRI
6.11 Conclusion and Future Directions
References
7. Early Prediction of Epileptic Seizure Using Deep Learning Algorithm
T. Jagadesh, A. Reethika, B. Jaishankar and M.S. Kanivarshini
7.1 Introduction
7.2 Methodology
7.3 Experimental Results
7.4 Taking Care of Children with Seizure Disorders
7.5 Ketogenic Diet
7.6 Vagus Nerve Stimulation (VNS)
7.7 Brain Surgeries
7.8 Conclusion
References
8. Brain–Computer Interface-Based Real-Time Movement of Upper Limb Prostheses Topic: Improving the Quality of the Elderly with Brain-Computer Interface
S. Vairaprakash and S. Rajagopal
8.1 Introduction
8.1.1 Motor Imagery Signal Decoding
8.2 Literature Survey
8.3 Methodology of Proposed Work
8.3.1 Proposed Control Scheme
8.3.2 One Versus All Adaptive Neural Type-2 Fuzzy Inference System (OVAANT2FIS)
8.3.3 Position Control of Robot Arm Using Hybrid BCI for Rehabilitation Purpose
8.3.4 Jaco Robot Arm
8.3.5 Scheme 1: Random Order Positional Control
8.4 Experiments and Data Processing
8.4.1 Feature Extraction
8.4.2 Performance Analysis of the Detectors
8.4.3 Performance of the Real Time Robot Arm Controllers
8.5 Discussion
8.6 Conclusion and Future Research Directions
References
9. Brain–Computer Interface-Assisted Automated Wheelchair Control Management-Cerebro: A BCI Application
Sudhendra Kambhamettu, Meenalosini Vimal Cruz, Anitha S., Sibi Chakkaravarthy S. and K. Nandeesh Kumar
9.1 Introduction
9.1.1 What is a BCI?
9.2 How Do BCI’s Work?
9.2.1 Measuring Brain Activity
9.2.1.1 Without Surgery
9.2.1.2 With Surgery
9.2.2 Mental Strategies
9.2.2.1 SSVEP
9.2.2.2 Neural Motor Imagery
9.3 Data Collection
9.3.1 Overview of the Data
9.3.2 EEG Headset
9.3.3 EEG Signal Collection
9.4 Data Pre-Processing
9.4.1 Artifact Removal
9.4.2 Signal Processing and Dimensionality Reduction
9.4.3 Feature Extraction
9.5 Classification
9.5.1 Deep Learning (DL) Model Pipeline
9.5.2 Architecture of the DL Model
9.5.3 Output Metrics of the Classifier
9.5.4 Deployment of DL Model
9.5.5 Control System
9.5.6 Control Flow Overview
9.6 Control Modes
9.6.1 Speech Mode
9.6.2 Blink Stimulus Mapping
9.6.3 Text Interface
9.6.4 Motion Mode
9.6.5 Motor Arrangement
9.6.6 Imagined Motion Mapping
9.7 Compilation of All Systems
9.8 Conclusion
References
10. Identification of Imagined Bengali Vowels from EEG Signals Using Activity Map and Convolutional Neural Network
Rajdeep Ghosh, Nidul Sinha and Souvik Phadikar
10.1 Introduction
10.1.1 Electroencephalography (EEG)
10.1.2 Imagined Speech or Silent Speech
10.2 Literature Survey
10.3 Theoretical Background
10.3.1 Convolutional Neural Network
10.3.2 Activity Map
10.4 Methodology
10.4.1 Data Collection
10.4.2 Pre-Processing
10.4.3 Feature Extraction
10.4.4 Classification
10.5 Results
10.6 Conclusion
Acknowledgment
References
11. Optimized Feature Selection Techniques for Classifying Electrocorticography Signals
B. Paulchamy, R. Uma Maheshwari, D. Sudarvizhi AP(Sr. G), R. Anandkumar AP(Sr. G) and Ravi G.
11.1 Introduction
11.1.1 Brain–Computer Interface
11.2 Literature Study
11.3 Proposed Methodology
11.3.1 Dataset
11.3.2 Feature Extraction Using Auto-Regressive (AR) Model and Wavelet Transform
11.3.2.1 Auto-Regressive Features
11.3.2.2 Wavelet Features
11.3.2.3 Feature Selection Methods
11.3.2.4 Information Gain (IG)
11.3.2.5 Clonal Selection
11.3.2.6 An Overview of the Steps of the CLONALG
11.3.3 Hybrid CLONALG
11.4 Experimental Results
11.4.1 Results of Feature Selection Using IG with Various Classifiers
11.4.2 Results of Optimizing Support Vector Machine Using CLONALG Selection
11.5 Conclusion
References
12. BCI – Challenges, Applications, and Advancements
R. Remya and Sumithra, M.G.
12.1 Introduction
12.1.1 BCI Structure
12.2 Related Works
12.3 Applications
12.4 Challenges and Advancements
12.5 Conclusion
References
Index
Back to Top
Preface
1. Introduction to Brain–Computer Interface: Applications and Challenges
Jyoti R. Munavalli, Priya R. Sankpal, Sumathi A. and Jayashree M. Oli
1.1 Introduction
1.2 The Brain – Its Functions
1.3 BCI Technology
1.3.1 Signal Acquisition
1.3.1.1 Invasive Methods
1.3.1.2 Non-Invasive Methods
1.3.2 Feature Extraction
1.3.3 Classification
1.3.3.1 Types of Classifiers
1.4 Applications of BCI
1.5 Challenges Faced During Implementation of BCI
References
2. Introduction: Brain–Computer Interface and Deep Learning
Muskan Jindal, Eshan Bajal and Areeba Kazim
2.1 Introduction
2.1.1 Current Stance of P300 BCI
2.2 Brain–Computer Interface Cycle
2.3 Classification of Techniques Used for Brain–Computer Interface
2.3.1 Application in Mental Health
2.3.2 Application in Motor-Imagery
2.3.3 Application in Sleep Analysis
2.3.4 Application in Emotion Analysis
2.3.5 Hybrid Methodologies
2.3.6 Recent Notable Advancements
2.4 Case Study: A Hybrid EEG-fNIRS BCI
2.5 Conclusion, Open Issues and Future Endeavors
References
3. Statistical Learning for Brain–Computer Interface
Lalit Kumar Gangwar, Ankit, John A. and Rajesh E.
3.1 Introduction
3.1.1 Various Techniques to BCI
3.1.1.1 Non-Invasive
3.1.1.2 Semi-Invasive
3.1.1.3 Invasive
3.2 Machine Learning Techniques to BCI
3.2.1 Support Vector Machine (SVM)
3.2.2 Neural Networks
3.3 Deep Learning Techniques Used in BCI
3.3.1 Convolutional Neural Network Model (CNN)
3.3.2 Generative DL Models
3.4 Future Direction
3.5 Conclusion
References
4. The Impact of Brain–Computer Interface on Lifestyle of Elderly People
Zahra Alidousti Shahraki and Mohsen Aghabozorgi Nafchi
4.1 Introduction
4.2 Diagnosing Diseases
4.3 Movement Control
4.4 IoT
4.5 Cognitive Science
4.6 Olfactory System
4.7 Brain-to-Brain (B2B) Communication Systems
4.8 Hearing
4.9 Diabetes
4.10 Urinary Incontinence
4.11 Conclusion
References
5. A Review of Innovation to Human Augmentation in Brain-Machine Interface – Potential, Limitation, and Incorporation of AI
T. Graceshalini, S. Rathnamala and M. Prabhanantha Kumar
5.1 Introduction
5.2 Technologies in Neuroscience for Recording and Influencing Brain Activity
5.2.1 Brain Activity Recording Technologies
5.2.1.1 A Non-Invasive Recording Methodology
5.2.1.2 An Invasive Recording Methodology
5.3 Neuroscience Technology Applications for Human Augmentation
5.3.1 Need for BMI
5.3.1.1 Need of BMI Individuals for Re-Establishing the Control and Communication of Motor
5.3.1.2 Brain-Computer Interface Noninvasive Research at Wadsworth Center
5.3.1.3 An Interface of Berlin Brain-Computer: Machine Learning-Dependent of User-Specific Brain States Detection
5.4 History of BMI
5.5 BMI Interpretation of Machine Learning Integration
5.6 Beyond Current Existing Methodologies: Nanomachine Learning BMI Supported
5.7 Challenges and Open Issues
5.8 Conclusion
References
6. Resting-State fMRI: Large Data Analysis in Neuroimaging
M. Menagadevi , S. Mangai, S. Sudha and D. Thiyagarajan
6.1 Introduction
6.1.1 Principles of Functional Magnetic Resonance Imaging (fMRI)
6.1.2 Resting State fMRI (rsfMRI) for Neuroimaging
6.1.3 The Measurement of Fully Connected and Construction of Default Mode Network (DMN)
6.2 Brain Connectivity
6.2.1 Anatomical Connectivity
6.2.2 Functional Connectivity
6.3 Better Image Availability
6.3.1 Large Data Analysis in Neuroimaging
6.3.2 Big Data rfMRI Challenges
6.3.3 Large rfMRI Data Software Packages
6.4 Informatics Infrastructure and Analytical Analysis
6.5 Need of Resting-State MRI
6.5.1 Cerebral Energetics
6.5.2 Signal to Noise Ratio (SNR)
6.5.3 Multi-Purpose Data Sets
6.5.4 Expanded Patient Populations
6.5.5 Reliability
6.6 Technical Development
6.7 rsfMRI Clinical Applications
6.7.1 Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD)
6.7.2 Fronto-Temporal Dementia (FTD)
6.7.3 Multiple Sclerosis (MS)
6.7.4 Amyotrophic Lateral Sclerosis (ALS) and Depression
6.7.5 Bipolar
6.7.6 Schizophrenia
6.7.7 Attention Deficit Hyperactivity Disorder (ADHD)
6.7.8 Multiple System Atrophy (MSA)
6.7.9 Epilepsy/Seizures
6.7.10 Pediatric Applications
6.8 Resting-State Functional Imaging of Neonatal Brain Image
6.9 Different Groups in Brain Disease
6.10 Learning Algorithms for Analyzing rsfMRI
6.11 Conclusion and Future Directions
References
7. Early Prediction of Epileptic Seizure Using Deep Learning Algorithm
T. Jagadesh, A. Reethika, B. Jaishankar and M.S. Kanivarshini
7.1 Introduction
7.2 Methodology
7.3 Experimental Results
7.4 Taking Care of Children with Seizure Disorders
7.5 Ketogenic Diet
7.6 Vagus Nerve Stimulation (VNS)
7.7 Brain Surgeries
7.8 Conclusion
References
8. Brain–Computer Interface-Based Real-Time Movement of Upper Limb Prostheses Topic: Improving the Quality of the Elderly with Brain-Computer Interface
S. Vairaprakash and S. Rajagopal
8.1 Introduction
8.1.1 Motor Imagery Signal Decoding
8.2 Literature Survey
8.3 Methodology of Proposed Work
8.3.1 Proposed Control Scheme
8.3.2 One Versus All Adaptive Neural Type-2 Fuzzy Inference System (OVAANT2FIS)
8.3.3 Position Control of Robot Arm Using Hybrid BCI for Rehabilitation Purpose
8.3.4 Jaco Robot Arm
8.3.5 Scheme 1: Random Order Positional Control
8.4 Experiments and Data Processing
8.4.1 Feature Extraction
8.4.2 Performance Analysis of the Detectors
8.4.3 Performance of the Real Time Robot Arm Controllers
8.5 Discussion
8.6 Conclusion and Future Research Directions
References
9. Brain–Computer Interface-Assisted Automated Wheelchair Control Management-Cerebro: A BCI Application
Sudhendra Kambhamettu, Meenalosini Vimal Cruz, Anitha S., Sibi Chakkaravarthy S. and K. Nandeesh Kumar
9.1 Introduction
9.1.1 What is a BCI?
9.2 How Do BCI’s Work?
9.2.1 Measuring Brain Activity
9.2.1.1 Without Surgery
9.2.1.2 With Surgery
9.2.2 Mental Strategies
9.2.2.1 SSVEP
9.2.2.2 Neural Motor Imagery
9.3 Data Collection
9.3.1 Overview of the Data
9.3.2 EEG Headset
9.3.3 EEG Signal Collection
9.4 Data Pre-Processing
9.4.1 Artifact Removal
9.4.2 Signal Processing and Dimensionality Reduction
9.4.3 Feature Extraction
9.5 Classification
9.5.1 Deep Learning (DL) Model Pipeline
9.5.2 Architecture of the DL Model
9.5.3 Output Metrics of the Classifier
9.5.4 Deployment of DL Model
9.5.5 Control System
9.5.6 Control Flow Overview
9.6 Control Modes
9.6.1 Speech Mode
9.6.2 Blink Stimulus Mapping
9.6.3 Text Interface
9.6.4 Motion Mode
9.6.5 Motor Arrangement
9.6.6 Imagined Motion Mapping
9.7 Compilation of All Systems
9.8 Conclusion
References
10. Identification of Imagined Bengali Vowels from EEG Signals Using Activity Map and Convolutional Neural Network
Rajdeep Ghosh, Nidul Sinha and Souvik Phadikar
10.1 Introduction
10.1.1 Electroencephalography (EEG)
10.1.2 Imagined Speech or Silent Speech
10.2 Literature Survey
10.3 Theoretical Background
10.3.1 Convolutional Neural Network
10.3.2 Activity Map
10.4 Methodology
10.4.1 Data Collection
10.4.2 Pre-Processing
10.4.3 Feature Extraction
10.4.4 Classification
10.5 Results
10.6 Conclusion
Acknowledgment
References
11. Optimized Feature Selection Techniques for Classifying Electrocorticography Signals
B. Paulchamy, R. Uma Maheshwari, D. Sudarvizhi AP(Sr. G), R. Anandkumar AP(Sr. G) and Ravi G.
11.1 Introduction
11.1.1 Brain–Computer Interface
11.2 Literature Study
11.3 Proposed Methodology
11.3.1 Dataset
11.3.2 Feature Extraction Using Auto-Regressive (AR) Model and Wavelet Transform
11.3.2.1 Auto-Regressive Features
11.3.2.2 Wavelet Features
11.3.2.3 Feature Selection Methods
11.3.2.4 Information Gain (IG)
11.3.2.5 Clonal Selection
11.3.2.6 An Overview of the Steps of the CLONALG
11.3.3 Hybrid CLONALG
11.4 Experimental Results
11.4.1 Results of Feature Selection Using IG with Various Classifiers
11.4.2 Results of Optimizing Support Vector Machine Using CLONALG Selection
11.5 Conclusion
References
12. BCI – Challenges, Applications, and Advancements
R. Remya and Sumithra, M.G.
12.1 Introduction
12.1.1 BCI Structure
12.2 Related Works
12.3 Applications
12.4 Challenges and Advancements
12.5 Conclusion
References
Index
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