Info
Brain-computer interface : using deep learning applications / / edited by M. G. Sumithra [and four others]
| Brain-computer interface : using deep learning applications / / edited by M. G. Sumithra [and four others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , [2023] |
| Descrizione fisica | 1 online resource (323 pages) |
| Disciplina | 616.8047547 |
| Soggetto topico |
Brain-computer interfaces
Deep learning (Machine learning) |
| ISBN |
1-119-85765-1
1-119-85764-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNINA-9910830951703321 |
| Hoboken, New Jersey : , : John Wiley & Sons, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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International conference on computing, communication, electrical and biomedical systems. / / Arulmurugan Ramu, Chow Chee Onn, M.G. Sumithra, editors
| International conference on computing, communication, electrical and biomedical systems. / / Arulmurugan Ramu, Chow Chee Onn, M.G. Sumithra, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022] |
| Descrizione fisica | 1 online resource (673 pages) |
| Disciplina | 610.28 |
| Collana | EAI/Springer innovations in communication and computing |
| Soggetto topico |
Biomedical engineering
Computer networks Telecommunication |
| ISBN | 3-030-86165-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910551830303321 |
| Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||