Brain-Computer Interface: Using Deep Learning Applications

Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1 Introduction to Brain–Computer Interface: Applications and Challenges
	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
Chapter 2 Introduction: Brain–Computer Interface and Deep Learning
	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
Chapter 3 Statistical Learning for Brain–Computer Interface
	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
Chapter 4 The Impact of Brain–Computer Interface on Lifestyle of Elderly People
	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
Chapter 5 A Review of Innovation to Human Augmentation in Brain-Machine Interface – Potential, Limitation, and Incorporation of AI
	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
Chapter 6 Resting-State fMRI: Large Data Analysis in Neuroimaging
	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
Chapter 7 Early Prediction of Epileptic Seizure Using Deep Learning Algorithm
	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
Chapter 8 Brain–Computer Interface-Based Real-Time Movement of Upper Limb Prostheses Topic: Improving the Quality of the Elderly with Brain-Computer Interface
	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
Chapter 9 Brain–Computer Interface-Assisted Automated Wheelchair Control Management-Cerebro: A BCI Application
	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
Chapter 10 Identification of Imagined Bengali Vowels from EEG Signals Using Activity Map and Convolutional Neural Network
	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
Chapter 11 Optimized Feature Selection Techniques for Classifying Electrocorticography Signals
	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
Chapter 12 BCI – Challenges, Applications, and Advancements
	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
EULA