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[null,null,"Titolo",null,"Brain-computer interface : using deep learning applications / / edited by M. G. Sumithra [and four others]"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, New Jersey : , : John Wiley & Sons, , [2023]"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"1-119-85765-1"]
[{"$nulls":4},"1-119-85764-3"]
[null,null,"Descrizione fisica",null,"1 online resource (323 pages)"]
[null,null,"Disciplina",null,"616.8047547"]
[null,null,"Soggetti",null,"Brain-computer interfaces"]
[{"$nulls":4},"Deep learning (Machine learning)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Brain-Computer Interface: Applications and Challenges -- 1.1 Introduction -- 1.2 The Brain - Its Functions -- 1.3 BCI Technology -- 1.3.1 Signal Acquisition -- 1.3.1.1 Invasive Methods -- 1.3.1.2 Non-Invasive Methods -- 1.3.2 Feature Extraction -- 1.3.3 Classification -- 1.3.3.1 Types of Classifiers -- 1.4 Applications of BCI -- 1.5 Challenges Faced During Implementation of BCI -- References -- Chapter 2 Introduction: Brain-Computer Interface and Deep Learning -- 2.1 Introduction -- 2.1.1 Current Stance of P300 BCI -- 2.2 Brain-Computer Interface Cycle -- 2.3 Classification of Techniques Used for Brain-Computer Interface -- 2.3.1 Application in Mental Health -- 2.3.2 Application in Motor-Imagery -- 2.3.3 Application in Sleep Analysis -- 2.3.4 Application in Emotion Analysis -- 2.3.5 Hybrid Methodologies -- 2.3.6 Recent Notable Advancements -- 2.4 Case Study: A Hybrid EEG-fNIRS BCI -- 2.5 Conclusion, Open Issues and Future Endeavors -- References -- Chapter 3 Statistical Learning for Brain-Computer Interface -- 3.1 Introduction -- 3.1.1 Various Techniques to BCI -- 3.1.1.1 Non-Invasive -- 3.1.1.2 Semi-Invasive -- 3.1.1.3 Invasive -- 3.2 Machine Learning Techniques to BCI -- 3.2.1 Support Vector Machine (SVM) -- 3.2.2 Neural Networks -- 3.3 Deep Learning Techniques Used in BCI -- 3.3.1 Convolutional Neural Network Model (CNN) -- 3.3.2 Generative DL Models -- 3.4 Future Direction -- 3.5 Conclusion -- References -- Chapter 4 The Impact of "]
[{"$nulls":4},"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."]
[{"$nulls":4},"Chapter 5 A Review of Innovation to Human Augmentation in Brain-Machine Interface - Potential, Limitation, and Incorporation of AI -- 5.1 Introduction -- 5.2 Technologies in Neuroscience for Recording and Influencing Brain Activity -- 5.2.1 Brain Activity Recording Technologies -- 5.2.1.1 A Non-Invasive Recording Methodology -- 5.2.1.2 An Invasive Recording Methodology -- 5.3 Neuroscience Technology Applications for Human Augmentation -- 5.3.1 Need for BMI -- 5.3.1.1 Need of BMI Individuals for Re-Establishing the Control and Communication of Motor -- 5.3.1.2 Brain-Computer Interface Noninvasive Research at Wadsworth Center -- 5.3.1.3 An Interface of Berlin Brain-Computer: Machine Learning-Dependent of User-Specific Brain States Detection -- 5.4 History of BMI -- 5.5 BMI Interpretation of Machine Learning Integration -- 5.6 Beyond Current Existing Methodologies: Nanomachine Learning BMI Supported -- 5.7 Challenges and Open Issues -- 5.8 Conclusion -- References -- Chapter 6 Resting-State fMRI: Large Data Analysis in Neuroimaging -- 6.1 Introduction -- 6.1.1 Principles of Functional Magnetic Resonance Imaging (fMRI) -- 6.1.2 Resting State fMRI (rsfMRI) for Neuroimaging -- 6.1.3 The Measurement of Fully Connected and Construction of Default Mode Network (DMN) -- 6.2 Brain Connectivity -- 6.2.1 Anatomical Connectivity -- 6.2.2 Functional Connectivity -- 6.3 Better Image Availability -- 6.3.1 Large Data Analysis in Neuroimaging -- 6.3.2 Big Data rfMRI Challenges -- 6.3.3 Large rfMRI Data Software Packages -- 6.4 Informatics Infrastructure and Analytical Analysis -- 6.5 Need of Resting-State MRI -- 6.5.1 Cerebral Energetics -- 6.5.2 Signal to Noise Ratio (SNR) -- 6.5.3 Multi-Purpose Data Sets -- 6.5.4 Expanded Patient Populations -- 6.5.5 Reliability -- 6.6 Technical Development -- 6.7 rsfMRI Clinical Applications."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"Research Directions -- References -- Chapter 9 Brain-Computer Interface-Assisted Automated Wheelchair Control Management-Cerebro: A BCI Application -- 9.1 Introduction."]
[{"$nulls":4},"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."]
[{"$nulls":4},"11.3.2.4 Information Gain (IG) -- 11.3.2.5 Clonal Selection -- 11.3.2.6 An Overview of the Steps of the CLONALG -- 11.3.3 Hybrid CLONALG -- 11.4 Experimental Results -- 11.4.1 Results of Feature Selection Using IG with Various Classifiers -- 11.4.2 Results of Optimizing Support Vector Machine Using CLONALG Selection -- 11.5 Conclusion -- References -- Chapter 12 BCI - Challenges, Applications, and Advancements -- 12.1 Introduction -- 12.1.1 BCI Structure -- 12.2 Related Works -- 12.3 Applications -- 12.4 Challenges and Advancements -- 12.5 Conclusion -- References -- Index -- EULA."]
[null,null,null,"Sommario/riassunto","BRAIN-COMPUTER INTERFACE It covers all the research prospects and recent advancements in the brain-computer interface using deep learning. The brain-computer interface (BCI) is an emerging technology that is developing to be more functional in practice. The aim is to establish, through experiences with electronic devices, a communication channel bridging the human neural networks within the brain to the external world. For example, creating communication or control applications for locked-in patients who have no control over their bodies will be one such use. Recently, from communication to marketing, recovery, care, mental state monitoring, and entertainment, the possible application areas have been expanding. Machine learning algorithms have advanced BCI technology in the last few decades, and in the sense of classification accuracy, performance standards have been greatly improved. For BCI to be effective in the real world, however, some problems remain to be solved. Research focusing on deep learning is anticipated to bring solutions in this regard. Deep learning has been applied in various fields such as computer vision and "]
[{"$nulls":4},"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."]