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Sahu M. Brain and Behavior Computing 2021
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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