{"product_id":"braincomputer-interface-9781119857204","title":"BrainComputer Interface","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Brain–Computer Interface: Applications and Challenges 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJyoti R. Munavalli, Priya R. Sankpal, Sumathi A. and Jayashree M. Oli\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 The Brain – Its Functions 3\u003c\/p\u003e \u003cp\u003e1.3 BCI Technology 3\u003c\/p\u003e \u003cp\u003e1.3.1 Signal Acquisition 5\u003c\/p\u003e \u003cp\u003e1.3.1.1 Invasive Methods 6\u003c\/p\u003e \u003cp\u003e1.3.1.2 Non-Invasive Methods 8\u003c\/p\u003e \u003cp\u003e1.3.2 Feature Extraction 10\u003c\/p\u003e \u003cp\u003e1.3.3 Classification 11\u003c\/p\u003e \u003cp\u003e1.3.3.1 Types of Classifiers 12\u003c\/p\u003e \u003cp\u003e1.4 Applications of BCI 13\u003c\/p\u003e \u003cp\u003e1.5 Challenges Faced During Implementation of BCI 17\u003c\/p\u003e \u003cp\u003eReferences 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Introduction: Brain–Computer Interface and Deep Learning 25\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMuskan Jindal, Eshan Bajal and Areeba Kazim\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 26\u003c\/p\u003e \u003cp\u003e2.1.1 Current Stance of P300 BCI 28\u003c\/p\u003e \u003cp\u003e2.2 Brain–Computer Interface Cycle 29\u003c\/p\u003e \u003cp\u003e2.3 Classification of Techniques Used for Brain–Computer Interface 38\u003c\/p\u003e \u003cp\u003e2.3.1 Application in Mental Health 38\u003c\/p\u003e \u003cp\u003e2.3.2 Application in Motor-Imagery 38\u003c\/p\u003e \u003cp\u003e2.3.3 Application in Sleep Analysis 39\u003c\/p\u003e \u003cp\u003e2.3.4 Application in Emotion Analysis 39\u003c\/p\u003e \u003cp\u003e2.3.5 Hybrid Methodologies 40\u003c\/p\u003e \u003cp\u003e2.3.6 Recent Notable Advancements 41\u003c\/p\u003e \u003cp\u003e2.4 Case Study: A Hybrid EEG-fNIRS BCI 46\u003c\/p\u003e \u003cp\u003e2.5 Conclusion, Open Issues and Future Endeavors 47\u003c\/p\u003e \u003cp\u003eReferences 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Statistical Learning for Brain–Computer Interface 63\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLalit Kumar Gangwar, Ankit, John A. and Rajesh E.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 64\u003c\/p\u003e \u003cp\u003e3.1.1 Various Techniques to BCI 64\u003c\/p\u003e \u003cp\u003e3.1.1.1 Non-Invasive 64\u003c\/p\u003e \u003cp\u003e3.1.1.2 Semi-Invasive 65\u003c\/p\u003e \u003cp\u003e3.1.1.3 Invasive 67\u003c\/p\u003e \u003cp\u003e3.2 Machine Learning Techniques to BCI 67\u003c\/p\u003e \u003cp\u003e3.2.1 Support Vector Machine (SVM) 69\u003c\/p\u003e \u003cp\u003e3.2.2 Neural Networks 69\u003c\/p\u003e \u003cp\u003e3.3 Deep Learning Techniques Used in BCI 70\u003c\/p\u003e \u003cp\u003e3.3.1 Convolutional Neural Network Model (CNN) 72\u003c\/p\u003e \u003cp\u003e3.3.2 Generative DL Models 73\u003c\/p\u003e \u003cp\u003e3.4 Future Direction 73\u003c\/p\u003e \u003cp\u003e3.5 Conclusion 74\u003c\/p\u003e \u003cp\u003eReferences 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 The Impact of Brain–Computer Interface on Lifestyle of Elderly People 77\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eZahra Alidousti Shahraki and Mohsen Aghabozorgi Nafchi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 78\u003c\/p\u003e \u003cp\u003e4.2 Diagnosing Diseases 79\u003c\/p\u003e \u003cp\u003e4.3 Movement Control 84\u003c\/p\u003e \u003cp\u003e4.4 IoT 85\u003c\/p\u003e \u003cp\u003e4.5 Cognitive Science 86\u003c\/p\u003e \u003cp\u003e4.6 Olfactory System 88\u003c\/p\u003e \u003cp\u003e4.7 Brain-to-Brain (B2B) Communication Systems 89\u003c\/p\u003e \u003cp\u003e4.8 Hearing 90\u003c\/p\u003e \u003cp\u003e4.9 Diabetes 91\u003c\/p\u003e \u003cp\u003e4.10 Urinary Incontinence 92\u003c\/p\u003e \u003cp\u003e4.11 Conclusion 93\u003c\/p\u003e \u003cp\u003eReferences 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A Review of Innovation to Human Augmentation in Brain-Machine Interface – Potential, Limitation, and Incorporation of AI 101 \u003cbr\u003e \u003c\/b\u003e\u003ci\u003eT. Graceshalini, S. Rathnamala and M. Prabhanantha Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 102\u003c\/p\u003e \u003cp\u003e5.2 Technologies in Neuroscience for Recording and Influencing Brain Activity 103\u003c\/p\u003e \u003cp\u003e5.2.1 Brain Activity Recording Technologies 104\u003c\/p\u003e \u003cp\u003e5.2.1.1 A Non-Invasive Recording Methodology 104\u003c\/p\u003e \u003cp\u003e5.2.1.2 An Invasive Recording Methodology 104\u003c\/p\u003e \u003cp\u003e5.3 Neuroscience Technology Applications for Human Augmentation 106\u003c\/p\u003e \u003cp\u003e5.3.1 Need for BMI 106\u003c\/p\u003e \u003cp\u003e5.3.1.1 Need of BMI Individuals for Re-Establishing the Control and Communication of Motor 107\u003c\/p\u003e \u003cp\u003e5.3.1.2 Brain-Computer Interface Noninvasive Research at Wadsworth Center 107\u003c\/p\u003e \u003cp\u003e5.3.1.3 An Interface of Berlin Brain-Computer: Machine Learning-Dependent of User-Specific Brain States Detection 107\u003c\/p\u003e \u003cp\u003e5.4 History of BMI 108\u003c\/p\u003e \u003cp\u003e5.5 BMI Interpretation of Machine Learning Integration 111\u003c\/p\u003e \u003cp\u003e5.6 Beyond Current Existing Methodologies: Nanomachine Learning BMI Supported 116\u003c\/p\u003e \u003cp\u003e5.7 Challenges and Open Issues 119\u003c\/p\u003e \u003cp\u003e5.8 Conclusion 120\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Resting-State fMRI: Large Data Analysis in Neuroimaging 127 \u003cbr\u003e \u003c\/b\u003e\u003ci\u003eM. Menagadevi , S. Mangai, S. Sudha and D. Thiyagarajan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 128\u003c\/p\u003e \u003cp\u003e6.1.1 Principles of Functional Magnetic Resonance Imaging (fMRI) 128\u003c\/p\u003e \u003cp\u003e6.1.2 Resting State fMRI (rsfMRI) for Neuroimaging 128\u003c\/p\u003e \u003cp\u003e6.1.3 The Measurement of Fully Connected and Construction of Default Mode Network (DMN) 129\u003c\/p\u003e \u003cp\u003e6.2 Brain Connectivity 129\u003c\/p\u003e \u003cp\u003e6.2.1 Anatomical Connectivity 129\u003c\/p\u003e \u003cp\u003e6.2.2 Functional Connectivity 130\u003c\/p\u003e \u003cp\u003e6.3 Better Image Availability 130\u003c\/p\u003e \u003cp\u003e6.3.1 Large Data Analysis in Neuroimaging 131\u003c\/p\u003e \u003cp\u003e6.3.2 Big Data rfMRI Challenges 133\u003c\/p\u003e \u003cp\u003e6.3.3 Large rfMRI Data Software Packages 134\u003c\/p\u003e \u003cp\u003e6.4 Informatics Infrastructure and Analytical Analysis 137\u003c\/p\u003e \u003cp\u003e6.5 Need of Resting-State MRI 137\u003c\/p\u003e \u003cp\u003e6.5.1 Cerebral Energetics 137\u003c\/p\u003e \u003cp\u003e6.5.2 Signal to Noise Ratio (SNR) 137\u003c\/p\u003e \u003cp\u003e6.5.3 Multi-Purpose Data Sets 138\u003c\/p\u003e \u003cp\u003e6.5.4 Expanded Patient Populations 138\u003c\/p\u003e \u003cp\u003e6.5.5 Reliability 138\u003c\/p\u003e \u003cp\u003e6.6 Technical Development 138\u003c\/p\u003e \u003cp\u003e6.7 rsfMRI Clinical Applications 139\u003c\/p\u003e \u003cp\u003e6.7.1 Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) 139\u003c\/p\u003e \u003cp\u003e6.7.2 Fronto-Temporal Dementia (FTD) 140\u003c\/p\u003e \u003cp\u003e6.7.3 Multiple Sclerosis (MS) 141\u003c\/p\u003e \u003cp\u003e6.7.4 Amyotrophic Lateral Sclerosis (ALS) and Depression 143\u003c\/p\u003e \u003cp\u003e6.7.5 Bipolar 144\u003c\/p\u003e \u003cp\u003e6.7.6 Schizophrenia 145\u003c\/p\u003e \u003cp\u003e6.7.7 Attention Deficit Hyperactivity Disorder (ADHD) 147\u003c\/p\u003e \u003cp\u003e6.7.8 Multiple System Atrophy (MSA) 147\u003c\/p\u003e \u003cp\u003e6.7.9 Epilepsy\/Seizures 147\u003c\/p\u003e \u003cp\u003e6.7.10 Pediatric Applications 149\u003c\/p\u003e \u003cp\u003e6.8 Resting-State Functional Imaging of Neonatal Brain Image 149\u003c\/p\u003e \u003cp\u003e6.9 Different Groups in Brain Disease 151\u003c\/p\u003e \u003cp\u003e6.10 Learning Algorithms for Analyzing rsfMRI 151\u003c\/p\u003e \u003cp\u003e6.11 Conclusion and Future Directions 154\u003c\/p\u003e \u003cp\u003eReferences 154\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Early Prediction of Epileptic Seizure Using Deep Learning Algorithm 157\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eT. Jagadesh, A. Reethika, B. Jaishankar and M.S. Kanivarshini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 158\u003c\/p\u003e \u003cp\u003e7.2 Methodology 164\u003c\/p\u003e \u003cp\u003e7.3 Experimental Results 169\u003c\/p\u003e \u003cp\u003e7.4 Taking Care of Children with Seizure Disorders 172\u003c\/p\u003e \u003cp\u003e7.5 Ketogenic Diet 172\u003c\/p\u003e \u003cp\u003e7.6 Vagus Nerve Stimulation (VNS) 172\u003c\/p\u003e \u003cp\u003e7.7 Brain Surgeries 173\u003c\/p\u003e \u003cp\u003e7.8 Conclusion 173\u003c\/p\u003e \u003cp\u003eReferences 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Brain–Computer Interface-Based Real-Time Movement of Upper Limb Prostheses Topic: Improving the Quality of the Elderly with Brain-Computer Interface 179\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Vairaprakash and S. Rajagopal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 180\u003c\/p\u003e \u003cp\u003e8.1.1 Motor Imagery Signal Decoding 181\u003c\/p\u003e \u003cp\u003e8.2 Literature Survey 182\u003c\/p\u003e \u003cp\u003e8.3 Methodology of Proposed Work 184\u003c\/p\u003e \u003cp\u003e8.3.1 Proposed Control Scheme 185\u003c\/p\u003e \u003cp\u003e8.3.2 One Versus All Adaptive Neural Type- 2 Fuzzy Inference System (OVAANT2FIS) 187\u003c\/p\u003e \u003cp\u003e8.3.3 Position Control of Robot Arm Using Hybrid BCI for Rehabilitation Purpose 187\u003c\/p\u003e \u003cp\u003e8.3.4 Jaco Robot Arm 189\u003c\/p\u003e \u003cp\u003e8.3.5 Scheme 1: Random Order Positional Control 189\u003c\/p\u003e \u003cp\u003e8.4 Experiments and Data Processing 192\u003c\/p\u003e \u003cp\u003e8.4.1 Feature Extraction 195\u003c\/p\u003e \u003cp\u003e8.4.2 Performance Analysis of the Detectors 197\u003c\/p\u003e \u003cp\u003e8.4.3 Performance of the Real Time Robot Arm Controllers 198\u003c\/p\u003e \u003cp\u003e8.5 Discussion 200\u003c\/p\u003e \u003cp\u003e8.6 Conclusion and Future Research Directions 202\u003c\/p\u003e \u003cp\u003eReferences 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Brain–Computer Interface-Assisted Automated Wheelchair Control Management-Cerebro: A BCI Application 205 \u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSudhendra Kambhamettu, Meenalosini Vimal Cruz, Anitha S., Sibi Chakkaravarthy S. and K. Nandeesh Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 206\u003c\/p\u003e \u003cp\u003e9.1.1 What is a BCI? 207\u003c\/p\u003e \u003cp\u003e9.2 How Do BCI’s Work? 207\u003c\/p\u003e \u003cp\u003e9.2.1 Measuring Brain Activity 208\u003c\/p\u003e \u003cp\u003e9.2.1.1 Without Surgery 208\u003c\/p\u003e \u003cp\u003e9.2.1.2 With Surgery 208\u003c\/p\u003e \u003cp\u003e9.2.2 Mental Strategies 209\u003c\/p\u003e \u003cp\u003e9.2.2.1 Ssvep 210\u003c\/p\u003e \u003cp\u003e9.2.2.2 Neural Motor Imagery 210\u003c\/p\u003e \u003cp\u003e9.3 Data Collection 211\u003c\/p\u003e \u003cp\u003e9.3.1 Overview of the Data 211\u003c\/p\u003e \u003cp\u003e9.3.2 EEG Headset 213\u003c\/p\u003e \u003cp\u003e9.3.3 EEG Signal Collection 214\u003c\/p\u003e \u003cp\u003e9.4 Data Pre-Processing 215\u003c\/p\u003e \u003cp\u003e9.4.1 Artifact Removal 216\u003c\/p\u003e \u003cp\u003e9.4.2 Signal Processing and Dimensionality Reduction 217\u003c\/p\u003e \u003cp\u003e9.4.3 Feature Extraction 217\u003c\/p\u003e \u003cp\u003e9.5 Classification 218\u003c\/p\u003e \u003cp\u003e9.5.1 Deep Learning (DL) Model Pipeline 219\u003c\/p\u003e \u003cp\u003e9.5.2 Architecture of the DL Model 220\u003c\/p\u003e \u003cp\u003e9.5.3 Output Metrics of the Classifier 221\u003c\/p\u003e \u003cp\u003e9.5.4 Deployment of DL Model 221\u003c\/p\u003e \u003cp\u003e9.5.5 Control System 223\u003c\/p\u003e \u003cp\u003e9.5.6 Control Flow Overview 223\u003c\/p\u003e \u003cp\u003e9.6 Control Modes 223\u003c\/p\u003e \u003cp\u003e9.6.1 Speech Mode 223\u003c\/p\u003e \u003cp\u003e9.6.2 Blink Stimulus Mapping 223\u003c\/p\u003e \u003cp\u003e9.6.3 Text Interface 225\u003c\/p\u003e \u003cp\u003e9.6.4 Motion Mode 225\u003c\/p\u003e \u003cp\u003e9.6.5 Motor Arrangement 225\u003c\/p\u003e \u003cp\u003e9.6.6 Imagined Motion Mapping 226\u003c\/p\u003e \u003cp\u003e9.7 Compilation of All Systems 226\u003c\/p\u003e \u003cp\u003e9.8 Conclusion 226\u003c\/p\u003e \u003cp\u003eReferences 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Identification of Imagined Bengali Vowels from EEG Signals Using Activity Map and Convolutional Neural Network 231\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eRajdeep Ghosh, Nidul Sinha and Souvik Phadikar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 232\u003c\/p\u003e \u003cp\u003e10.1.1 Electroencephalography (EEG) 233\u003c\/p\u003e \u003cp\u003e10.1.2 Imagined Speech or Silent Speech 233\u003c\/p\u003e \u003cp\u003e10.2 Literature Survey 234\u003c\/p\u003e \u003cp\u003e10.3 Theoretical Background 238\u003c\/p\u003e \u003cp\u003e10.3.1 Convolutional Neural Network 238\u003c\/p\u003e \u003cp\u003e10.3.2 Activity Map 240\u003c\/p\u003e \u003cp\u003e10.4 Methodology 242\u003c\/p\u003e \u003cp\u003e10.4.1 Data Collection 243\u003c\/p\u003e \u003cp\u003e10.4.2 Pre-Processing 244\u003c\/p\u003e \u003cp\u003e10.4.3 Feature Extraction 245\u003c\/p\u003e \u003cp\u003e10.4.4 Classification 247\u003c\/p\u003e \u003cp\u003e10.5 Results 249\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 252\u003c\/p\u003e \u003cp\u003eAcknowledgment 252\u003c\/p\u003e \u003cp\u003eReferences 252\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Optimized Feature Selection Techniques for Classifying Electrocorticography Signals 255\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eB. Paulchamy, R. Uma Maheshwari, D. Sudarvizhi AP(Sr. G), R. Anandkumar AP(Sr. G) and Ravi G.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 256\u003c\/p\u003e \u003cp\u003e11.1.1 Brain–Computer Interface 256\u003c\/p\u003e \u003cp\u003e11.2 Literature Study 258\u003c\/p\u003e \u003cp\u003e11.3 Proposed Methodology 260\u003c\/p\u003e \u003cp\u003e11.3.1 Dataset 261\u003c\/p\u003e \u003cp\u003e11.3.2 Feature Extraction Using Auto-Regressive (AR) Model and Wavelet Transform 261\u003c\/p\u003e \u003cp\u003e11.3.2.1 Auto-Regressive Features 261\u003c\/p\u003e \u003cp\u003e11.3.2.2 Wavelet Features 262\u003c\/p\u003e \u003cp\u003e11.3.2.3 Feature Selection Methods 262\u003c\/p\u003e \u003cp\u003e11.3.2.4 Information Gain (IG) 263\u003c\/p\u003e \u003cp\u003e11.3.2.5 Clonal Selection 263\u003c\/p\u003e \u003cp\u003e11.3.2.6 An Overview of the Steps of the Clonalg 264\u003c\/p\u003e \u003cp\u003e11.3.3 Hybrid CLONALG 265\u003c\/p\u003e \u003cp\u003e11.4 Experimental Results 268\u003c\/p\u003e \u003cp\u003e11.4.1 Results of Feature Selection Using IG with Various Classifiers 272\u003c\/p\u003e \u003cp\u003e11.4.2 Results of Optimizing Support Vector Machine Using CLONALG Selection 274\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 276\u003c\/p\u003e \u003cp\u003eReferences 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 BCI – Challenges, Applications, and Advancements 279\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eR. Remya and Sumithra, M.G.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 279\u003c\/p\u003e \u003cp\u003e12.1.1 BCI Structure 280\u003c\/p\u003e \u003cp\u003e12.2 Related Works 281\u003c\/p\u003e \u003cp\u003e12.3 Applications 282\u003c\/p\u003e \u003cp\u003e12.4 Challenges and Advancements 297\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 299\u003c\/p\u003e \u003cp\u003eReferences 299\u003c\/p\u003e \u003cp\u003eIndex 303\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49528865980759,"sku":"9781119857204","price":153.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119857204.jpg?v=1731873331","url":"https:\/\/bookcurl.com\/products\/braincomputer-interface-9781119857204","provider":"Book Curl","version":"1.0","type":"link"}