Muutke küpsiste eelistusi

Brain and Behavior Computing [Kõva köide]

Edited by (IIITB, India), Edited by (National Institute of Technology Raipur, India)
  • Formaat: Hardback, 400 pages, kõrgus x laius: 234x156 mm, kaal: 725 g, 91 Tables, black and white; 131 Line drawings, black and white; 78 Halftones, black and white; 209 Illustrations, black and white
  • Ilmumisaeg: 24-Jun-2021
  • Kirjastus: CRC Press
  • ISBN-10: 0367552973
  • ISBN-13: 9780367552978
Teised raamatud teemal:
Brain and Behavior ComputingSuurem pilt
  • Formaat: Hardback, 400 pages, kõrgus x laius: 234x156 mm, kaal: 725 g, 91 Tables, black and white; 131 Line drawings, black and white; 78 Halftones, black and white; 209 Illustrations, black and white
  • Ilmumisaeg: 24-Jun-2021
  • Kirjastus: CRC Press
  • ISBN-10: 0367552973
  • ISBN-13: 9780367552978
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367552978.html
Märksõnad:
"Brain and Behavior Computing provides an insight into the functions of the human brain. This book provides emphasis on brain and behavior computing with different modalities available such as signal and image processing, data science, statistics, distributed computing including fundamentals, model, algorithms, case studies, and research scope. It further illustrates brain signals sources and how the brain signal can process, manipulate and transform in different domains to extract information about the physiological condition of the brain. Emphasis is on real challenges in brain signal processing for variety of applications for analysis, classification, clustering and identification"--

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, distributed computing and 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 its workings.

  • 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 xix
Acknowledgments xxi
Editors' Biographies xxiii
List of Contributors xxv
Chapter 1 Simulation Tools for Brain Signal Analysis 1(28)
1.1 Introduction
1(1)
1.2 Toolboxes for Analysis of Brain Signal (EEG/MEG) Recordings
2(25)
1.2.1 EEGLAB-Toolbox
2(6)
1.2.1.1 EEGLAB-GUI
3(1)
1.2.1.2 Data Importing
3(3)
1.2.1.3 EEGLAB Data-Structure
6(2)
1.2.2 Brain Computer Interface Lab Toolbox (BCILab)
8(3)
1.2.2.1 Installation
8(1)
1.2.2.2 BCILab-GUI
9(1)
1.2.2.3 BCILab Scripting
9(2)
1.2.3 PyEEG
11(1)
1.2.3.1 Example
11(1)
1.2.4 Fieldtrip Toolbox
12(5)
1.2.4.1 Installation
12(1)
1.2.4.2 Reading the MEG/EEG Recording Using Fieldtrip
13(3)
1.2.4.3 Reading Event Information
16(1)
1.2.4.4 Re-referencing EEG Recordings
16(1)
1.2.4.5 Visualize Electrode Locations
16(1)
1.2.4.6 Example
17(1)
1.2.5 BrainNet Viewer
17(15)
1.2.5.1 Installation
18(1)
1.2.5.2 File Menu
18(4)
1.2.5.3 Option Menu
22(4)
1.2.5.4 Visualize Menu
26(1)
1.2.5.5 Tools Menu
26(1)
1.3 Conclusion
27(1)
References
27(2)
Chapter 2 Processing Techniques and Analysis of Brain Sensor Data Using Electroencephalography 29(32)
2.1 Introduction.
29(1)
2.2 Building Blocks of The Human Brain
30(2)
2.3 Brain Signal Acquisition Techniques
32(1)
2.3.1 Local Field Potential (LFP)
32(1)
2.3.2 Positron Emission Tomography (PET)
33(1)
2.3.3 Electroencephalography (EEG)
33(1)
2.3.4 Functional Near-Infrared Spectroscopy (fNIRS)
33(1)
2.4 Electroencephalogram (EEG)
33(7)
2.4.1 EEG Sensor Data Collection
34(3)
2.4.2 Applications of EEG Signals
37(1)
2.4.3 EEG Signal Pre Processing
38(2)
2.4.3.1 ICA Algorithm
39(1)
2.5 Statistical Analysis of Brain Sensor Data
40(4)
2.5.1 Parametric Tes
40(1)
2.5.2 Nonparametric Test
40(4)
2.6 EEG Sensor Data Analysis
44(5)
2.6.1 Time-Domain Analysis
44(1)
2.6.2 Frequency Domain Analysis
45(2)
2.6.2.1 Fast Fourier Transform
46(1)
2.6.3 Time-Frequency Domain Analysis
47(2)
2.6.3.1 Complex Monet Wavelet
48(1)
2.7 Extreme Learning Machine (ELM)
49(3)
2.7.1 ELM Algorithm
51(1)
2.7.2 Dataset Description
51(1)
2.7.3 Results
52(1)
2.8 Conclusion
52(4)
References
56(5)
Chapter 3 Application of Machine-Learning Techniques in Electroencephalography Signals 61(24)
3.1 Introduction
61(1)
3.2 Brain and Electroencephalography (EEG)
61(6)
3.2.1 Human Brain
62(1)
3.2.2 Fundamentals of Brain Activities and Their Electrical Nature
62(1)
3.2.3 Principles of EEG and What They Measure
63(1)
3.2.4 Importance of EEG and Its Signal Processing Features
64(3)
3.3 Introduction to Machine Learning techniques
67(8)
3.3.1 Conventional Machine Learning Algorithms for Classification
70(1)
3.3.2 Deep Learning Algorithms for Classification
71(3)
3.3.2.1 Convolution Layer
72(1)
3.3.2.2 Activation Function
72(1)
3.3.2.3 Pooling Layer
73(1)
3.3.2.4 Post Processing of Predicted Label
73(1)
3.3.3 Deciding on a Classification Algorithm
74(1)
3.4 Neuroscience Application of Machine Learning Using EEG Signals
75(6)
3.4.1 Seizure Detection
75(3)
3.4.1.1 Background: What Are Seizures?
75(1)
3.4.1.2 Application: How Can ML Help Predict Seizure from EEG?
76(2)
3.4.2 Sleep Stage Detection
78(11)
3.4.2.1 Background: What Is Sleep?
78(2)
3.4.2.2 Application: How Can ML Help Classify Sleep Stages from EEG?
80(1)
3.5 Summary
81(1)
References
81(4)
Chapter 4 Revolution of Brain Computer Interface: An Introduction 85(26)
4.1 Introduction
85(1)
4.2 Neuroimaging Approaches in BCIs
86(1)
4.3 Types of BCIs
87(2)
4.4 Neurophysiologic Signals
89(2)
4.4.1 Event-Related Potential (ERP)
89(1)
4.4.2 Neuronal Ensemble Activity (NEA)
89(1)
4.4.3 Oscillatory Brain Activity (OBA)
89(1)
4.4.4 Visual Evoked Potential (VEP)
89(1)
4.4.5 P300 Evoked Potential
90(1)
4.4.6 Slow Cortical Potential (SCP)
90(1)
4.4.7 Sensorimotor Rhythm
91(1)
4.5 Signal Processing and Machine Learning
91(1)
4.5.1 Frequency Domain Feature (FDF)
91(1)
4.5.2 Time Domain Feature (TDF)
91(1)
4.5.3 Machine Learning Feature (MLF)
92(1)
4.5.4 Spatial Domain Feature (SDF)
92(1)
4.6 The Challenges in the Brain Computer Interface
92(1)
4.6.1 Information Transfer Rate ("ITR")
92(1)
4.6.2 High Error Rate ("HER")
92(1)
4.6.3 Autonomy
92(1)
4.6.4 Cognitive Burden
93(1)
4.6.5 Training Process
93(1)
4.7 The Development of Biosensing Techniques for BCI Applications
93(3)
4.7.1 Wet Sensor
93(1)
4.7.2 Dry Biosensor
94(1)
4.7.3 Nano- and Microtechnology Sensors
95(1)
4.7.4 Multimodality Sensors
95(1)
4.8 Integration of Sensing Devices and Biosensors into BCI Systems 9(
(6
4.8.1 Present Developments in Biosensing Device Technologies
96(1)
4.8.1.1 Essential System Design
96(1)
4.8.1.2 Simple Front-end
96(1)
4.8.1.3 Transmission Medium
96(1)
4.8.2 Advances of Future Bio-sensing Technique in BCI
97(1)
4.8.2.1 Scaling Down of Power Sources
97(1)
4.8.2.2 Real Life Applications of Human Brain Imaging
97(1)
4.9 BCI Technology
98(1)
4.9.1 Functional Near Infrared Technology (fNIR)
98(1)
4.9.2 Functional Near Infrared (fNIR) Device
99(1)
4.9.3 Shut Circled, Input Managed, fNIR Based Brain Computer Interface
99(1)
4.10 Applications
99(3)
4.10.1 Communications
99(1)
4.10.2 Entertainments
100(1)
4.10.3 Educational and Self-Regulation
100(1)
4.10.4 Medical Applications
101(1)
4.10.4.1 Detection and Diagnosis
101(1)
4.10.4.2 Prevention
101(1)
4.10.4.3 Restoration and Rehabilitation
101(1)
4.10.5 Other BCI Applications
102(1)
4.11 Future Scope
102(3)
4.11.1 The Future of BCI Technologies
102(3)
4.11.1.1 Direct Control (DC)
102(1)
4.11.1.2 Circuitous Control or Indirect Control (CC or IC)
103(1)
4.11.1.3 Communications
103(1)
4.11.1.4 Brain-Process Modification (BPM)
104(1)
4.11.1.5 Mental State Detection (MSD)
104(1)
4.11.1.6 Opportunistic State-Based Detection (OSBD)
104(1)
4.11.2 Future BCI Applications Based on Advanced Biosensing Technology
105(1)
4.12 Conclusion
105(1)
References
106(5)
Chapter 5 Signal Modeling Using Spatial Filtering and Matching Wavelet Feature Extraction for Classification of Brain Activity Pattern 111(30)
5.1 Introduction
111(1)
5.1.1 Sensorimotor Rhythms (SMR): An Efficient Input to BCI
112(1)
5.2 Signal Processing Strategies for BCI
112(10)
5.2.1 Signal Modeling Methods
112(3)
5.2.1.1 Surface Laplacian (SL) Counteracting the Volume Conduction
113(2)
5.2.2 Feature Extraction Strategies
115(3)
5.2.2.1 Wavelet Transform
116(1)
5.2.2.2 Methods for Wavelet Function Selection
117(1)
5.2.3 Feature Formation
118(1)
5.2.4 Feature Selection Strategies
119(1)
5.2.5 Classifier
120(3)
5.2.5.1 Support Vector Machine for Classification
120(1)
5.2.5.2 Discriminant Analysis for Classification
121(1)
5.2.5.3 k-Nearest Neighbor (k-NN)
122(1)
5.3 Dataset Used
122(1)
5.4 Implementation Methodology
123(8)
5.4.1 Implementation of Surface Laplacian
123(2)
5.4.2 Wavelet Function Selection Methodology
125(16)
5.4.2.1 Level of Wavelet Decomposition
125(3)
5.4.2.2 Wavelet Function Selection
128(2)
5.4.2.3 Optimized Feature Extraction and Classification
130(1)
5.5 Results
131(4)
5.6 Concluding Remarks
135(1)
References
135(6)
Chapter 6 Study and Analysis of the Visual P300 Speller on Neurotypical Subjects 141(22)
6.1 Introduction
141(2)
6.1.1 Goals and Objectives
142(1)
6.2 Literature Review
143(2)
6.3 Dataset Description
145(1)
6.4 Electroencephalography
146(1)
6.4.1 Event Related Potential
147(1)
6.5 P300 Speller
147(1)
6.6 Manual Feature Extraction
148(1)
6.7 Classification Techniques
148(1)
6.8 Model Fitting (Support Vector Machine)
148(1)
6.9 Proposed Methodology
148(3)
6.9.1 Manual Approach
149(1)
6.9.1.1 Feature Extraction
149(1)
6.9.1.2 Feature Selection
150(1)
6.9.1.3 Classification
150(1)
6.9.2 Semi-Automated Approach
150(1)
6.10 Result and Analysis
151(7)
6.10.1 Results through Manual Approach
151(2)
6.10.2 Results through the Semi-Automated Approach
153(1)
6.10.3 Comparison of the Two Techniques
154(4)
6.11 Conclusion
158(1)
Acknowledgments
159(1)
References
159(4)
Chapter 7 Effective Brain Computer Interface Based on the Adaptive-Rate Processing and Classification of Motor Imagery Tasks 163(26)
7.1 Introduction and Background
163(1)
7.2 Motivation and Contribution
164(1)
7.3 Electroencephalography in Healthcare and BCI
165(2)
7.4 The Proposed Approach
167(10)
7.4.1 Dataset
167(1)
7.4.2 Reconstruction
168(1)
7.4.3 The Event-Driven A/D Converter (EDADC)
169(2)
7.4.4 The Event-Driven Segmentation
171(1)
7.4.5 Extraction of Features
171(3)
7.4.5.1 Extraction of Time Domain Features
172(1)
7.4.5.2 Extraction of Frequency Domain Features
173(1)
7.4.6 Machine Learning Algorithms
174(3)
7.4.6.1 Support Vector Machine (SVM)
175(2)
7.5 The Performance Evaluation Measures
177(2)
7.5.1 Compression Ratio
177(1)
7.5.2 Computational Complexity
177(1)
7.5.3 Classification Accuracy
178(11)
7.5.3.1 Accuracy (Acc)
178(1)
7.5.3.2 Specificity (Sp)
179(1)
7.6 Experimental Results
179(3)
7.7 Discussion
182(1)
7.8 Conclusion
183(1)
Acknowledgments
184(1)
References
184(5)
Chapter 8 EEG-Based BCI Systems for Neurorehabilitation Applications 189(32)
8.1 Introduction
189(2)
8.1.1 Classification of BCI Systems
190(1)
8.1.1.1 Invasive, Semi-Invasive and Non-Invasive BCI Systems
190(1)
8.1.1.2 Exogenous and Endogenous BCI Systems
190(1)
8.1.1.3 Synchronous and Asynchronous BCI Systems
191(1)
8.1.1.4 Dependent and Independent BCI Systems
191(1)
8.2 EEG Based BCI System Architecture For Neurorehabilitation
191(2)
8.2.1 Pre-rehabilitation Phase
192(1)
8.2.2 Rehabilitation Phase
193(1)
8.2.3 Post-rehabilitation Phase
193(1)
8.3 Types of BCI Paradigms
193(16)
8.3.1 Steady-State Visual Evoked Potential (SSVEP)
193(6)
8.3.1.1 Introduction
193(1)
8.3.1.2 Case Study for SSVEP-BCI Implementation in Neurorehabilitation: BCI Based 3D Virtual Playground for the Attention Deficit Hyperactivity Disorder (ADHD) Patients
194(5)
8.3.2 P300
199(5)
8.3.2.1 Introduction
199(1)
8.3.2.2 Case Study for P300-BCI Implementation in Neurorehabilitation: Adaptive Filtering for Detection of User-Independent Event Related Potentials in BCIs
199(5)
8.3.3 Motor Imagery (MI)
204(5)
8.3.3.1 Introduction
204(1)
8.3.3.2 Case Study for MI-BCI Implementation in Neurorehabilitation: Brain Computer Interface in Cognitive Neurorehabilitation
205(4)
8.4 Types of BCI Controlled Motion Functioning Units
209(2)
8.4.1 Functional Electric Stimulation (FES)
209(1)
8.4.2 Robotics Assistance
209(1)
8.4.3 VR Based Hybrid Unit
210(1)
8.5 Neurorehabilitaion Applications of BCI Systems
211(3)
8.6 Conclusion
214(1)
References
215(6)
Chapter 9 Scalp EEG Classification Using TQWT-Entropy Features for Epileptic Seizure Detection 221(22)
9.1 Introduction
221(1)
9.2 Material and Methods
222(3)
9.2.1 EEG Data
222(2)
9.2.2 TQWT-Based EEG Decomposition
224(1)
9.3 Feature Extraction Methodology
225(4)
9.3.1 Approximate Entropy (AE) Estimation
225(2)
9.3.2 Sample Entropy (SE) Estimation
227(1)
9.3.3 Renyi's Entropy (RE) Estimation
228(1)
9.3.4 Permutation Entropy (PE) Estimation
228(1)
9.4 Soft Computing Techniques
229(1)
9.5 Results and Discussion
229(10)
9.6 Conclusion
239(1)
References
239(4)
Chapter 10 An Efficient Single-Trial Classification Approach for Devanagari Script-Based Visual P300 Speller Using Knowledge Distillation and Transfer Learning 243(24)
10.1 Introduction
243(3)
10.2 Methodology
246(7)
10.2.1 The Dataset
246(2)
10.2.2 Details of the Proposed Architecture
248(4)
10.2.2.1 Block-1 (L0): Input
249(1)
10.2.2.2 Block-2 (L1-L2): Temporal Information
249(1)
10.2.2.3 Block-3 (L3-L5): Spatial Information
249(2)
10.2.2.4 Block-4 (L6-L7): Class Prediction
251(1)
10.2.3 Knowledge Distillation (Teacher-Student Network)
252(1)
10.3 Experimental Setup
253(1)
10.3.1 Transfer Learning
253(1)
10.3.1.1 Inter-subject Transfer Learning
254(1)
10.3.1.2 Inter-trial Transfer Learning
254(1)
10.3.2 Training settings
254(1)
10.4 Results
254(6)
10.4.1 ShallowCNN
255(1)
10.4.1.1 Cross-Subject Analysis
255(1)
10.4.1.2 Within-Subject Analysis
256(1)
10.4.2 EEGNet
256(2)
10.4.2.1 Cross-Subject Analysis
256(1)
10.4.2.2 Within-Subject Analysis
257(1)
10.4.3 Proposed Channel-wise EEGNet
258(2)
10.4.3.1 Cross-Subject Analysis
258(1)
10.4.3.2 Within-Subject Analysis
259(1)
10.5 Discussion
260(3)
10.5.1 Hypothesis 1: Channel-Mix Versus Channel-Wise Convolution
260(1)
10.5.2 Hypothesis 2: Effect of Knowledge Distillation
261(1)
10.5.3 Hypothesis 3: Data Balancing Approaches
261(1)
10.5.4 Hypotheses 4 & 5: Effect of Transfer Learning
262(1)
10.6 Conclusion
263(1)
Acknowledgments
263(1)
References
264(3)
Chapter 11 Deep Learning Algorithms for Brain Image Analysis 267(26)
11.1 Introduction
267(1)
11.2 Brain Image Data and Strategies
268(1)
11.3 Deep Neural Networks
269(6)
11.3.1 Perceptron
269(2)
11.3.2 FeedForward Neural Networks
271(2)
11.3.3 Convolutional Neural Networks
273(2)
11.4 Image Registration
275(5)
11.4.1 Rigid Registration
276(1)
11.4.2 Deformable Registration
277(1)
11.4.3 Experiments
278(1)
11.4.3.1 Impact of Loss Function
278(1)
11.4.4 Multimodal Registration
279(1)
11.4.5 Atlas Construction
280(1)
11.5 Image Segmentation
280(5)
11.5.1 Ischemic Stroke Lesion Segmentation
282(1)
11.5.2 Brain Tumor Segmentation
283(1)
11.5.3 Multiple Sclerosis Lesion Segmentation
283(1)
11.5.4 Hippocampus Segmentation
284(1)
11.5.5 Experiments
284(1)
11.6 Image Classification
285(2)
11.6.1 Schizophrenia Diagnosis
287(1)
11.6.2 Diagnosis of Alzheimer Disease
287(1)
11.7 Conclusion
287(1)
Notes
288(1)
References
288(5)
Chapter 12 Evolutionary Optimization-Based Two-Dimensional Elliptical FIR Filters for Skull Stripping in Brain Imaging and Disorder Detection 293(16)
12.1 Introduction
293(2)
12.2 Pre-processing
295(1)
12.2.1 Image Enhancement
295(1)
12.2.2 Image Denoise
295(1)
12.2.3 Skull Strapping
296(1)
12.3 Filter Design for Image Enhancement (Formulation of Objectives)
296(1)
12.4 Filter Design for Image Denoising (Formulation of Objectives)
297(1)
12.5 Filter Design for Skull Stripping (Formulation of Objectives)
297(1)
12.6 ABC Algorithm
298(1)
12.7 QABC Algorithm
299(1)
12.8 Skull Stripping and Brain Tumor Localization Architecture
300(1)
12.9 Results and Discussion
301(2)
12.9.1 Examples of Skull Stripping
302(1)
12.9.2 Examples of Tumor Segmentation
302(1)
12.9.3 Tumor Localization
302(1)
12.10 Conclusion
303(4)
References
307(2)
Chapter 13 EEG-Based Neurofeedback Game for Focus Level Enhancement 309(24)
13.1 Introduction
309(5)
13.1.1 Brain Computer Interface and Neurofeedback
310(1)
13.1.2 Types of NF and Brain Rhythms
311(1)
13.1.3 EEG Based Games
312(2)
13.2 Neurofeedback Game Design
314(7)
13.2.1 System Framework
314(1)
13.2.2 EEG Data Acquisition Module
315(1)
13.2.3 EEG Game Design with Unity 3D
315(1)
13.2.4 The Car Driving Game
316(2)
13.2.4.1 The EEG Headset Panel
316(1)
13.2.4.2 The Stages
317(1)
13.2.4.3 The Controls
318(1)
13.2.5 Computation of FL and Scores
318(3)
13.2.5.1 Computation of FL
318(2)
13.2.5.2 Computation of Scores
320(1)
13.3 Neurofeedback Session
321(2)
13.3.1 Subjects
321(1)
13.3.2 Mental Command Training
321(1)
13.3.3 Neurofeedback Session through Game Playing
322(1)
13.4 Results and Discussion
323(6)
13.4.1 Effect of Age of the Participants
323(1)
13.4.2 Effect of Gender of the Participants
323(5)
13.4.3 Effect of Game Elements
328(1)
13.5 Conclusion and Future Recommendations
329(1)
References
330(3)
Chapter 14 Detecting K-Complexes in Brain Signals Using WSST2-DETOKS 333(24)
14.1 Introduction
333(2)
14.2 Synchro-Squeezed Wavelet Transform
335(2)
14.3 Second-order Wavelet Based SST
337(4)
14.3.1 Numerical Implementation of WSST2
339(1)
14.3.2 Computing WSST2
340(1)
14.4 Detection of Sleep Spindles and K-Complexes (DETOKS)
341(1)
14.4.1 Sparse Optimization
342(1)
14.5 WSST2-DETOKS for K-Complex Detection
342(5)
14.5.1 Problem Formulation
343(1)
14.5.2 Algorithm
344(3)
14.6 Data Description
347(1)
14.6.1 Proposed Scoring Method
348(1)
14.7 Results
348(5)
14.7.1 Statistical Analysis
349(4)
14.8 Conclusion
353(1)
Acknowledgments
354(1)
References
354(3)
Chapter 15 Directed Functional Brain Networks: Characterization of Information Flow Direction during Cognitive Function Using Non-Linear Granger Causality 357(22)
15.1 Introduction
357(2)
15.2 Directed Functional Brain Networks Construction
359(1)
15.3 Granger Causality
359(2)
15.4 Directed FBNs Analysis
361(2)
15.4.1 Connectivity Density
361(1)
15.4.2 Clustering Coefficient
361(1)
15.4.3 Local Information Measure
362(1)
15.5 Methods
363(6)
15.5.1 Participants in the Cognitive Experiments
363(1)
15.5.2 EEG Data Collection
363(2)
15.5.2.1 Baseline - Eyes Open (EOP)
364(1)
15.5.2.2 Cognitive Task Relating to Visual Search (VS)
364(1)
15.5.2.3 Web Search Cognitive Task (Around 5-10 Minutes)
365(1)
15.5.3 EEG Signal Pre-processing
365(2)
15.5.4 A Framework for the Computation and Analysis of Information Flow Direction Patterns
367(1)
15.5.5 Information Flow Direction Patterns (IFDP) for Weighted Directed Network
367(2)
15.6 Results and Discussion
369(4)
15.6.1 Binary Directed Functional Brain Network
369(2)
15.6.1.1 Connectivity Density
370(1)
15.6.1.2 Clustering Coefficient
370(1)
15.6.2 Weighted Directed Functional Brain Network
371(8)
15.6.2.1 Weighted IFDP Analysis
372(1)
15.6.2.2 Local Information Measure
373(1)
15.7 Conclusion
373(2)
References
375(4)
Chapter 16 Student Behavior Modeling and Context Acquisition: A Ubiquitous Learning Framework 379(12)
16.1 Introduction
379(1)
16.2 A Survey on Context Modeling Frameworks
379(4)
16.2.1 Context Modeling Approaches
379(2)
16.2.1.1 Various Context Modeling Approaches in Ubiquitous Learning Environments
380(1)
16.2.2 Context Acquisition, Reasoning, and Dissemination in Ubiquitous Learning
381(2)
16.2.2.1 Student Learning Behavioral Model
382(1)
16.2.2.2 Subject Domain
382(1)
16.2.2.3 Context Acquisition and Dissemination
382(1)
16.3 Proposed Modeling of Student Learning Behavior, Subject Domain, and Context Acquisition in Ubiquitous Learning Environments
383(5)
16.3.1 Student Context Information Representation
383(1)
16.3.2 Supporting Structure of Context Acquisition
384(1)
16.3.3 Student Modeling
384(1)
16.3.4 Learning Behavior Goal Elements of A Student
385(1)
16.3.5 Subject Domain Modeling
386(1)
16.3.6 Context Information Modeling in Ubiquitous Learning Systems
387(1)
16.3.7 Context Information Modeling For Specific Student's Accessing the System
388(1)
16.4 Evaluation of Proposed Model In Various Learning Scenarios
388(2)
16.4.1 Professional Student Accessing the System
388(1)
16.4.2 Novice Student to Check on Negative Emotions
388(2)
16.5 Conclusion
390(1)
References
390(1)
Index 391
Mridu Sahu has completed her graduation in Computer Science and Engineering in 2004 from Maulana Azad National Institute of Technology, Bhopal. She completed her post graduation Master of Technology in Computer Science and Engineering from RIT, Raipur in 2011 and completed the Ph.D. in Computer Science and Engineering in 2018 from National Institute of Technology Raipur, India. She is having more than 10-year experiences in teaching, presently she is working as an Assistant Professor in department of Information Technology, NIT Raipur, India. She has published more than 25 research articles in various journals and conferences and book chapters in the field of Data Mining, Brain Computer Interface, Sensor devices and Visual Mining Techniques.



G R Sinha is Adjunct Professor at International Institute of Information Technology (IIIT) Bangalore and currently deputed as Professor at Myanmar Institute of Information Technology (MIIT) Mandalay Myanmar. He obtained his B.E. (Electronics Engineering) and M.Tech. (Computer Technology) with Gold Medal from National Institute of Technology Raipur, India. He received his Ph.D. in Electronics & Telecommunication Engineering from Chhattisgarh Swami Vivekanand Technical University (CSVTU) Bhilai, India. He is Visiting Professor (Honorary) in Sri Lanka Technological Campus Colombo for one year 2019-2020. He has published 250 research papers, book chapters and books at International and National level that includes Biometrics published by Wiley India, a subsidiary of John Wiley; Medical Image Processing published by Prentice Hall of India and 05 Edited books on Cognitive Science-Two Volumes (Elsevier), Optimization Theory (IOP) and Biometrics (Springer). He is active reviewer and editorial member of more than 12 Reputed International Journals such IEEE Transactions on Image Processing, Elsevier Computer Methods and Programs in Biomedicine, Springer Journal of Neural Computing and Applications etc. He has teaching and research experience of 21 years. He has been Dean of Faculty and Executive Council Member of CSVTU and currently a member of Senate of MIIT. Dr Sinha has been delivering ACM lectures as ACM Distinguished Speaker in the field of DSP since 2017 across the world. His few more important assignments include Expert Member for Vocational Training Programme by Tata Institute of Social Sciences (TISS) for Two Years (2017-2019); Chhattisgarh Representative of IEEE P Sub-Section Executive Council (2016-2019); Distinguished Speaker in the field of Digital Image Processing by Computer Society of India (2015). He is recipient of many awards and recognitions like TCS Award 2014 for Outstanding contributions in Campus Commune of TCS, Rajaram Bapu Patil ISTE National Award 2013 for Promising Teacher in Technical Education by ISTE New Delhi, Emerging Chhattisgarh Award 2013, Engineer of the Year Award 2011, Young Engineer Award 2008, Young Scientist Award 2005, IEI Expert Engineer Award 2007, ISCA Young Scientist Award 2006 Nomination and Deshbandhu Merit Scholarship for 05 years. He served as Distinguished IEEE Lecturer in IEEE India council for Bombay section. He is Senior Member of IEEE and Fellow of IETE India.