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sahu m brain behavior computing 2021

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Textbook in PDF format Brain and Behavior Computing offers insights into the functions of the human brain. This book provides an emphasis on brain and behavior computing with different modalities available such as signal processing, image processing, data sciences, statistics further it includes fundamental, mathematical model, algorithms, case studies, and future research scopes. It further illustrates brain signal sources and how the brain signal can process, manipulate, and transform in different domains allowing researchers and professionals to extract information about the physiological condition of the brain. Emphasizes real challenges in brain signal processing for a variety of applications for analysis, classification, and clustering. Discusses data sciences and its applications in brain computing visualization. Covers all the most recent tools for analysing the brain and it’s working. Describes brain modeling and all possible machine learning methods and their uses. Augments the use of data mining and machine learning to brain computer interface (BCI) devices. Includes case studies and actual simulation examples. This book is aimed at researchers, professionals, and graduate students in image processing and computer vision, biomedical engineering, signal processing, and brain and behavior computing. Preface Acknowledgments Editors' Biographies Contributors Simulation Tools for Brain Signal Analysis Introduction Toolboxes for Analysis of Brain Signal (EEG/MEG) Recordings EEGLAB-Toolbox EEGLAB-GUI Data Importing Load Data as MATLAB Array EEGLAB Data-Structure Brain Computer Interface Lab Toolbox (BCILab) Installation BCILab-GUI BCILab Scripting Example PyEEG Example Fieldtrip Toolbox Installation Reading the MEG/EEG Recording Using Fieldtrip Reading Event Information Re-Referencing EEG Recordings Visualize Electrode Locations Example BrainNet Viewer Installation File Menu Load Surface File Load Node File Load Edge File Option Menu Layout Option Panel Global Option Panel Surface Option Panel Node Option Panel Edge Option Panel Volume Option Panel Image Option Panel Visualize Menu Tools Menu Conclusion References Processing Techniques and Analysis of Brain Sensor Data Using Electroencephalography Introduction Building Blocks of the Human Brain Brain Signal Acquisition Techniques Local Field Potential (LFP) Positron Emission Tomography (PET) Electroencephalography (EEG) Functional Near-Infrared Spectroscopy (fNIRS) Electroencephalogram (EEG) EEG Sensor Data Collection Applications of EEG Signals EEG Signal Preprocessing ICA Algorithm Statistical Analysis of Brain Sensor Data Parametric Test Nonparametric Test EEG Sensor Data Analysis Time-Domain Analysis Frequency Domain Analysis Fast Fourier Transform Time-Frequency Domain Analysis Complex Morlet Wavelet Extreme Learning Machine (ELM) ELM Algorithm Dataset Description Results Conclusion References Application of Machine-Learning Techniques in Electroencephalography Signals Introduction Brain and Electroencephalography (EEG) Human Brain Fundamentals of Brain Activities and Their Electrical Nature Principles of EEG and What They Measure Importance of EEG and Its Signal Processing Features Introduction to Machine Learning Techniques Conventional Machine Learning Algorithms for Classification Deep Learning Algorithms for Classification Convolution Layer Activation Function Pooling Layer Post Processing of Predicted Label Deciding on a Classification Algorithm Neuroscience Application of Machine Learning Using EEG Signals Seizure Detection Background: What Are Seizures? Application: How Can ML Help Predict Seizure From EEG? Sleep Stage Detection Background: What Is Sleep? Application: How Can ML Help Classify Sleep Stages From EEG? Summary References Revolution of Brain Computer Interface: An Introduction Introduction Neuroimaging Approaches in BCIs Types of BCIs Neurophysiologic Signals Event-Related Potential (ERP) Neuronal Ensemble Activity (NEA) Oscillatory Brain Activity (OBA) Visual Evoked Potential (VEP) P Evoked Potential Slow Cortical Potential (SCP) Sensorimotor Rhythm Signal Processing and Machine Learning Frequency Domain Feature (FDF) Time Domain Feature (TDF) Machine Learning Feature (MLF) Spatial Domain Feature (SDF) The Challenges in The Brain Computer Interface: Information Transfer Rate ("ITR") High Error Rate ("HER") Autonomy Cognitive Burden Training Process The Development of Biosensing Techniques for BCI Applications Wet Sensor Dry Biosensor Nano- and Microtechnology Sensors Multimodality Sensors Integration of Sensing Devices and Biosensors Into BCI Systems Present Developments in Biosensing Device Technologies Essential System Design Simple Front-End Transmission Medium Advances of Future Bio-sensing Technique in BCI Scaling Down of Power Sources Real Life Applications of Human Brain Imaging BCI Technology Functional Near Infrared Technology (fNIR) Functional Near Infrared (fNIR) Device Shut Circled, Input Managed, fNIR Based Brain Computer Interface Applications Communications Entertainments Educational and Self-Regulation Medical Applications Detection and Diagnosis Prevention Restoration and Rehabilitation Other BCI Applications Future Scope The Future of BCI Technologies Direct Control (DC) Circuitous Control or Indirect Control (CC or IC) Communications Brain-Process Modification (BPM) Mental State Detection (MSD) Opportunistic State-Based Detection (OSBD) Future BCI Applications Based on Advanced Biosensing Technology Conclusions References Signal Modeling Using Spatial Filtering and Matching Wavelet Feature Extraction for Classification of Brain Activity Patterns Introduction Sensorimotor Rhythms (SMR): An Efficient Input to BCI Signal Processing Strategies for BCI Signal Modelling Methods Surface Laplacian (SL) Counteracting the Volume Conduction Feature Extraction Strategies Wavelet Transform Methods for Wavelet Function Selection Feature Formation Feature Selection Strategies Classifier Support Vector Machine for Classification Discriminant Analysis for Classification K-Nearest Neighbor (k-NN) Dataset Used Implementation Methodology Implementation of Surface Laplacian Wavelet Function Selection Methodology Level of Wavelet Decomposition Wavelet Function Selection Optimized Feature Extraction and Classification Results Concluding Remarks References Study and Analysis of the Visual P Speller on Neurotypical Subjects Introduction Goals and Objectives Literature Review Dataset Description Electroencephalography Event Related Potential P Speller Manual Feature Extraction Classification Techniques Model Fitting (Support Vector Machine) Proposed Methodology Manual Approach Feature Extraction Feature Selection Classification Semi-Automated Approach Result and Analysis Results through Manual Approach Results through the Semi-Automated Approach Comparison of the Two Techniques Conclusion Acknowledgments References Effective Brain Computer Interface Based on the Adaptive-Rate Processing and Classification of Motor Imagery Tasks Introduction and Background Motivation and Contribution Electroencephalography in Healthcare and BCI The Proposed Approach Dataset Reconstruction The Event-Driven A/D Converter (EDADC) The Event-Driven Segmentation Extraction of Features Extraction of Time Domain Features Extraction of Frequency Domain Features Machine Learning Algorithms Support Vector Machine (SVM) k-Nearest Neighbors (k-NN) The Performance Evaluation Measures Compression Ratio Computational Complexity Classification Accuracy Accuracy (Acc) Specificity (Sp) Experimental Results Discussion Conclusion Acknowledgments References EEG-Based BCI Systems for Neurorehabilitation Applications Introduction Classification of BCI Systems Invasive, Semi-Invasive and Non-Invasive BCI Systems Exogenous and Endogenous BCI Systems Synchronous and Asynchronous BCI Systems Dependent and Independent BCI Systems EEG Based BCI System Architecture for Neurorehabilitation Pre-Rehabilitation Phase Rehabilitation Phase Post-rehabilitation Phase Types of BCI Paradigms Steady-State Visual Evoked Potential (SSVEP) Introduction Case Study for SSVEP-BCI Implementation in Neurorehabilitation: BCI Based D Virtual Playground for the Attention Deficit Hyperactivity Disorder (ADHD) Patients Methodology and Experimental Setup Experimental Results Case Study Conclusion P Introduction Case Study for P-BCI Implementation in Neurorehabilitation: Adaptive Filtering for Detection of User-Independent Event Related Potentials in BCIs Methodology and Experimental Setup Experimental Results Case Study Conclusion Motor Imagery (MI) Introduction Case Study for MI-BCI Implementation in Neurorehabilitation: Brain Computer Interface in Cognitive Neurorehabilitation Methodology and Experimental Setup Experimental Results Case Study Conclusion Types of BCI Controlled Motion Functioning Units Functional Electric Stimulation (FES) Robotics Assistance VR Based Hybrid Unit Neurorehabilitation Applications of BCI Systems Conclusion References Scalp EEG Classification Using TQWT-Entropy Features for Epileptic Seizure Detection Introduction Material and Methods EEG Data TQWT-Based EEG Decomposition Feature Extraction Methodology Approximate Entropy (AE) Estimation Sample Entropy (SE) Estimation Renyi's Entropy (RE) Estimation Permutation Entropy (PE) Estimation Soft Computing Techniques Results and Discussion Conclusion References An Efficient Single-Trial Classification Approach for Devanagari Script-Based Visual P Speller Using Knowledge Distillation and Transfer Learning Introduction Methodology The Dataset Details of the Proposed Architecture Block- (L): Input Block- (L-L): Temporal Information Block- (L-L): Spatial Information Block- (L-L): Class Prediction Knowledge Distillation (Teacher-Student Network) Experimental Setup Transfer Learning Inter-Subject Transfer Learning Inter-Trial Transfer Learning Training Settings Results ShallowCNN Cross-Subject Analysis Within-Subject Analysis EEGNet Cross-Subject Analysis Within-Subject Analysis Proposed Channel-Wise EEGNet Cross-Subject Analysis Within-Subject Analysis Discussion Hypothesis : Channel-Mix Versus Channel-Wise Convolution Hypothesis : Effect of Knowledge Distillation Hypothesis : Data Balancing Approaches Hypotheses & : Effect of Transfer Learning Conclusion Acknowledgment References Deep Learning Algorithms for Brain Image Analysis Introduction Brain Image Data and Strategies Deep Neural Networks Perceptron FeedForward Neural Networks Convolutional Neural Networks Image Registration Rigid Registration Deformable Registration Experiments Impact of Loss Function Multimodal Registration Atlas Construction Image Segmentation Ischemic Stroke Lesion Segmentation Brain Tumor Segmentation Multiple Sclerosis Lesion Segmentation Hippocampus Segmentation Experiments Image Classification Schizophrenia Diagnosis Diagnosis of Alzheimer Disease Conclusion Notes References Evolutionary Optimization-Based Two-Dimensional Elliptical FIR Filters for Skull Stripping in Brain Imaging and Disorder Detection Introduction Pre-Processing Image Enhancement Image Denoise Skull Stripping Filter Design for Image Enhancement (Formulation of Objectives) Filter Design for Image Denoising (Formulation of Objectives) Filter Design for Skull Stripping (Formulation of Objectives) ABC Algorithm QABC Algorithm Skull Stripping and Brain Tumor Localization Architecture Results and Discussion Examples of Skull Stripping Examples of Tumor Segmentation Tumor Localization Conclusion References EEG-Based Neurofeedback Game for Focus Level Enhancement Introduction Brain Computer Interface and Neurofeedback Types of NF and Brain Rhythms EEG-Based Games Neurofeedback Game Design System Framework EEG Data Acquisition Module EEG Game Design with Unity D The Car Driving Game The EEG Headset Panel The Stages The Controls Computation of FL and Scores Computation of FL Computation of Scores Neurofeedback Session Subjects Mental Command Training Neurofeedback Sessions through Game Playing Results and Discussion Effect of Age of the Participants Effect of Gender of the Participants Effect of Game Elements Conclusion and Future Recommendations Acknowledgment References Detecting K-Complexes in Brain Signals Using WSST-DETOKS Introduction Synchro-Squeezed Wavelet Transform Second-Order Wavelet-Based SST Numerical Implementation of WSST Computing WSST Detection of Sleep Spindles and K-Complexes (DETOKS) Sparse Optimization WSST-DETOKS for K-Complex Detection Problem Formulation Algorithm Data Description Proposed Scoring Method Results Statistical Analysis Conclusion Acknowledgment References Directed Functional Brain Networks: Characterization of Information Flow Direction during Cognitive Function Using Non-Linear Granger Causality Introduction Directed Functional Brain Network Construction Granger Causality Directed FBNs ANALYSIS Connectivity Density Clustering Coefficient Local Information Measure Methods Participants in the Cognitive Experiments EEG Data Collection Baseline - Eyes Open (EOP) Cognitive Task Relating to Visual Search (VS) Web Search Cognitive Task (Around - Minutes) EEG Signal Pre-processing A Framework for the Computation and Analysis of Information Flow Direction Patterns Information Flow Direction Patterns (IFDP) for Weighted Directed Network Results and Discussion Binary Directed Functional Brain Network Connectivity Density Clustering Coefficient Weighted Directed Functional Brain Network Weighted IFDP Analysis Local Information Measure Conclusion References Student Behavior Modeling and Context Acquisition: A Ubiquitous Learning Framework Introduction A Survey on Context Modeling Frameworks Context Modeling Approaches Various Context Modeling Approaches in Ubiquitous Learning Environments Context Acquisition, Reasoning, and Dissemination in Ubiquitous Learning Environments Student Learning Behavioral Model Subject Domain Context Acquisition and Dissemination Proposed Modeling of Student Learning Behavior, Subject Domain, and Context Acquisition in Ubiquitous Learning Environments Student Context Information Representation Supporting Structure of Context Acquisition Student Modeling Learning Behavior Goal Elements of a Student Subject Domain Modeling Context Information Modeling in Ubiquitous Learning Systems Context Information Modeling for Specific Student's Accessing the System Evaluation of Proposed Model in Various Learning Scenarios Professional Student Accessing the System Novice Student to Check on Negative Emotions Conclusion References Index

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sahu m brain behavior computing 2021