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El. knyga: Cognitive Modeling of Human Memory and Learning: A Non-invasive Brain-Computer Interfacing Approach

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  • Formatas: EPUB+DRM
  • Serija: IEEE Press
  • Išleidimo metai: 02-Sep-2020
  • Leidėjas: Wiley-IEEE Press
  • Kalba: eng
  • ISBN-13: 9781119705918
Kitos knygos pagal šią temą:
Cognitive Modeling of Human Memory and Learning: A Non-invasive Brain-Computer Interfacing ApproachDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Serija: IEEE Press
  • Išleidimo metai: 02-Sep-2020
  • Leidėjas: Wiley-IEEE Press
  • Kalba: eng
  • ISBN-13: 9781119705918
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"This book models human memory from a cognitive standpoint by utilizing brain activations acquired from the cortex by electroencephalographic (EEG) and functional near-infrared-spectroscopic (f-NIRs) means. It begins with an overview of the early models of memory. The authors then propose a simplistic model of Working Memory (WM) built with fuzzy Hebbian learning. A second perspective of memory models is concerned with Short-Term Memory (STM)-modeling in the context of 2-dimensional object-shape reconstruction from visually examined memorized instances. A third model assesses the subjective motor learning skill in driving from erroneous motor actions. Other models introduce a novel strategy of designing a two-layered deep Long Short-Term Memory (LSTM) classifier network and also deal with cognitive load assessment in motor learning tasks associated with driving. The book ends with concluding remarks based on principles and experimental results acquired in previous chapters"--

Proposes computational models of human memory and learning using a brain-computer interfacing (BCI) approach 

Human memory modeling is important from two perspectives. First, the precise fitting of the model to an individual's short-term or working memory may help in predicting memory performance of the subject in future. Second, memory models provide a biological insight to the encoding and recall mechanisms undertaken by the neurons present in active brain lobes, participating in the memorization process. This book models human memory from a cognitive standpoint by utilizing brain activations acquired from the cortex by electroencephalographic (EEG) and functional near-infrared-spectroscopic (f-NIRs) means.   

Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach begins with an overview of the early models of memory. The authors then propose a simplistic model of Working Memory (WM) built with fuzzy Hebbian learning. A second perspective of memory models is concerned with Short-Term Memory (STM)-modeling in the context of 2-dimensional object-shape reconstruction from visually examined memorized instances. A third model assesses the subjective motor learning skill in driving from erroneous motor actions. Other models introduce a novel strategy of designing a two-layered deep Long Short-Term Memory (LSTM) classifier network and also deal with cognitive load assessment in motor learning tasks associated with driving. The book ends with concluding remarks based on principles and experimental results acquired in previous chapters.  

  • Examines the scope of computational models of memory and learning with special emphasis on classification of memory tasks by deep learning-based models  
  • Proposes two algorithms of type-2 fuzzy reasoning: Interval Type-2 fuzzy reasoning (IT2FR) and General Type-2 Fuzzy Sets (GT2FS)  
  • Considers three classes of cognitive loads in the motor learning tasks for driving learners  

Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach will appeal to researchers in cognitive neuro-science and human/brain-computer interfaces. It is also beneficial to graduate students of computer science/electrical/electronic engineering. 

Preface xi
Acknowledgments xvii
About the Authors xix
1 Introduction to Brain-Inspired Memory and Learning Models 1(50)
1.1 Introduction
1(2)
1.2 Philosophical Contributions to Memory Research
3(7)
1.2.1 Atkinson and Shiffrin's Model
4(1)
1.2.2 Tveter's Model
5(1)
1.2.3 Tulving's Model
6(1)
1.2.4 The Parallel and Distributed Processing (PDP) Approach
6(2)
1.2.5 Procedural and Declarative Memory
8(2)
1.3 Brain-Theoretic Interpretation of Memory Formation
10(6)
1.3.1 Coding for Memory
10(2)
1.3.2 Memory Consolidation
12(2)
1.3.3 Location of Stored Memories
14(1)
1.3.4 Isolation of Information in Memory
15(1)
1.4 Cognitive Maps
16(1)
1.5 Neural Plasticity
17(1)
1.6 Modularity
18(1)
1.7 The Cellular Process Behind STM Formation
18(2)
1.8 LTM Formation
20(1)
1.9 Brain Signal Analysis in the Context of Memory and Learning
20(15)
1.9.1 Association of EEG α and theta Band with Memory Performances
21(3)
1.9.2 Oscillatory β and γ Frequency Band Activation in STM Performance
24(1)
1.9.3 Change in EEG Band Power with Changing Working Memory Load
24(3)
1.9.4 Effects of Electromagnetic Field on the EEG Response of Working Memory
27(1)
1.9.5 EEG Analysis to Discriminate Focused Attention and WM Performance
28(1)
1.9.6 EEG Power Changes in Memory Repetition Effect
29(3)
1.9.7 Correlation Between LTM Retrieval and EEG Features
32(2)
1.9.8 Impact of Math Anxiety on WM Response: An EEG Study
34(1)
1.10 Memory Modeling by Computational Intelligence Techniques
35(4)
1.11 Scope of the Book
39(4)
References
43(8)
2 Working Memory Modeling Using Inverse Fuzzy Relational Approach 51(42)
2.1 Introduction
52(2)
2.2 Problem Formulation and Approach
54(16)
2.2.1 Independent Component Analysis as a Source Localization Tool
55(3)
2.2.2 Independent Component Analysis vs. Principal Component Analysis
58(1)
2.2.3 Feature Extraction
58(1)
2.2.4 Phase 1: WM Modeling
59(3)
2.2.4.1 Step I: WM Modeling of Subject Using EEG Signals During Full Face Encoding and Recall from Specific Part of Same Face
60(2)
2.2.4.2 Step II: WM Modeling of Subject Using EEG Signals During Full Face Encoding and Recall from All Parts of Same Face
62(1)
2.2.5 Phase 2: WM Analysis
62(3)
2.2.6 Finding Max-Min Compositional Inverse of Weight Matrix Wk
65(5)
2.3 Experiments and Performance Analysis
70(15)
2.3.1 Experimental Set-up
71(2)
2.3.2 Source Localization Using eLORETA
73(1)
2.3.3 Pre-processing
74(1)
2.3.4 Selection of EEG Features
74(3)
2.3.5 WM Model Consistency Across Partial Face Stimuli
77(1)
2.3.6 Inter-person Variability of W
77(1)
2.3.7 Variation in Imaging Attributes
77(7)
2.3.8 Comparative Analysis with Existing Fuzzy Inverse Relations
84(1)
2.4 Discussion
85(1)
2.5 Conclusions
86(2)
References
88(5)
3 Short-Term Memory Modeling in Shape-Recognition Task by Type-2 Fuzzy Deep Brain Learning 93(44)
3.1 Introduction
94(2)
3.2 System Overview
96(5)
3.3 Brain Functional Mapping Using Type-2 Fuzzy DBLN
101(12)
3.3.1 Overview of Type-2 Fuzzy Sets
103(1)
3.3.2 Type-2 Fuzzy Mapping and Parameter Adaptation by Perceptron-Like Learning
104(6)
3.3.2.1 Construction of the Proposed Interval Type-2 Fuzzy Membership Function (IT2MF)
104(1)
3.3.2.2 Construction of IT2FS-Induced Mapping Function
105(2)
3.3.2.3 Secondary Membership Function Computation of Proposed GT2FS
107(1)
3.3.2.4 Proposed General Type-2 Fuzzy Mapping
108(2)
3.3.3 Perceptron-Like Learning for Weight Adaptation
110(1)
3.3.4 Training of the Proposed Shape-Reconstruction Architecture
111(2)
3.3.5 The Test Phase of the Memory Model
113(1)
3.4 Experiments and Results
113(7)
3.4.1 Experimental Set-up
113(3)
3.4.2 Experiment 1: Validation of the STM Model with Respect to Error Metric ξ
116(1)
3.4.3 Experiment 2: Similar Encoding by a Subject for Similar Input Object Shapes
116(1)
3.4.4 Experiment 3: Study of Subjects' Learning Ability with Increasing Complexity in Object Shape
117(1)
3.4.5 Experiment 4: Convergence Time of the Weight Matrix G for Increased Complexity of the Input Shape Stimuli
118(1)
3.4.6 Experiment 5: Abnormality in G matrix for the Subjects with Brain Impairment
119(1)
3.5 Biological Implications
120(2)
3.6 Performance Analysis
122(5)
3.6.1 Performance Analysis of the Proposed T2FS Methods
123(1)
3.6.2 Computational Performance Analysis of the Proposed T2FS Methods
123(1)
3.6.3 Statistical Validation Using Wilcoxon Signed-Rank Test
124(2)
3.6.4 Optimal Parameter Selection and Robustness Study
126(1)
3.7 Conclusions
127(3)
References
130(7)
4 EEG Analysis for Subjective Assessment of Motor Learning Skill in Driving Using Type-2 Fuzzy Reasoning 137(38)
4.1 Introduction
138(2)
4.2 System Overview
140(7)
4.2.1 Rule Design to Determine the Degree of Learning
141(3)
4.2.2 Single Trial Detection of Brain Signals
144(2)
4.2.2.1 Feature Extraction
144(1)
4.2.2.2 Feature Selection
145(1)
4.2.2.3 Classification
145(1)
4.2.3 Type-2 Fuzzy Reasoning
146(1)
4.2.4 Training and Testing of the Classifiers
146(1)
4.3 Determining Type and Degree of Learning by Type-2 Fuzzy Reasoning
147(10)
4.3.1 Preliminaries on IT2FS and GT2FS
147(1)
4.3.2 Proposed Reasoning Method 1: CIT2FS-Based Reasoning
148(2)
4.3.3 Computation of Percentage Normalized Degree of Learning
150(1)
4.3.4 Optimal A Selection in IT2FS Reasoning
151(1)
4.3.5 Proposed Reasoning Method 2: Triangular Vertical Slice (TVS)-Based CGT2FS Reasoning
151(3)
4.3.5.1 Closed General Type-2 Fuzzy Inference Generation
151(3)
4.3.5.2 Time complexity
154(1)
4.3.6 Proposed Reasoning Method 3: CGT2FS Reasoning with Gaussian Secondary MF
154(3)
4.3.6.1 Time-Complexity
156(1)
4.4 Experiments and Results
157(7)
4.4.1 The Experimental Set-up
157(1)
4.4.2 Stimulus Presentation
157(1)
4.4.3 Experiment 1: Source Localization Using eLORETA
158(1)
4.4.4 Experiment 2: Validation of the Rules
159(1)
4.4.5 Experiment 3: Pre-processing and Artifact Removal Using ICA
159(4)
4.4.6 Experiment 4: N400 Old/New Effect Observation over the Successive Trials
163(1)
4.4.7 Experiment 5: Selection of the Discriminating EEG Features Using PCA
163(1)
4.5 Performance Analysis and Statistical Validation
164(5)
4.5.1 Performance Analysis of the LSVM Classifiers
164(1)
4.5.2 Robustness Study
165(1)
4.5.3 Performance Analysis of the Proposed T2FS Reasoning Methods
166(1)
4.5.4 Computational Performance Analysis of the Proposed T2FS Reasoning Methods
166(2)
4.5.5 Statistical Validation Using Wilcoxon Signed-Rank Test
168(1)
4.6 Conclusions
169(1)
References
169(6)
5 EEG Analysis to Decode Human Memory Responses in Face Recognition Task Using Deep LSTM Network 175(28)
5.1 Introduction
176(3)
5.2 CSP Modeling
179(4)
5.2.1 The Standard CSP Algorithm
179(1)
5.2.2 The Proposed CSP Algorithm
180(3)
5.3 Proposed LSTM Classifier with Attention Mechanism
183(5)
5.3.1 Attention Mechanism in Each LSTM Unit
184(4)
5.4 Experiments and Results
188(8)
5.4.1 Experimental Set-up
188(1)
5.4.2 Experiment 1: Activated Brain Region Selection Using eLORETA
188(2)
5.4.3 Experiment 2: Detection of the ERP Signals Associated with the Familiar and Unfamiliar Face Discrimination
190(1)
5.4.4 Experiment 3: Performance Analysis of the Proposed CSP Algorithm as a Feature Extraction Technique
191(1)
5.4.5 Experiment 4: Performance Analysis of the Proposed LSTM-Based Classifier
192(2)
5.4.6 Experiment 5: Classifier Performance Analysis with Varying EEG Time-Window Length
194(1)
5.4.7 Statistical Validation of the Proposed LSTM Classifier Using McNemar's Test
195(1)
5.5 Conclusions
196(1)
References
197(6)
6 Cognitive Load Assessment in Motor Learning Tasks by Near-Infrared Spectroscopy Using Type-2 Fuzzy Sets 203(36)
6.1 Introduction
203(3)
6.2 Principles and Methodologies
206(5)
6.2.1 Normalization of the Raw Data
206(1)
6.2.2 Pre-processing
207(1)
6.2.3 Feature Extraction
208(1)
6.2.4 Training Instance Generation for Offline Training
208(1)
6.2.5 Feature Selection Using Evolutionary Algorithm
209(1)
6.2.6 Classifier Training and Testing
210(1)
6.3 Classifier Design
211(8)
6.3.1 Preliminaries on IT2FS and GT2FS
211(1)
6.3.2 IT2FS-Induced Classifier Design
212(4)
6.3.3 GT2FS-Induced Classifier Design
216(3)
6.4 Experiments and Results
219(7)
6.4.1 Experimental Set-up
219(1)
6.4.2 Participants
219(2)
6.4.3 Stimulus Presentation for Online Classification
221(1)
6.4.4 Experiment 1: Demonstration of Decreasing Cognitive Load with Increasing Learning Epochs for Similar Stimulus
221(2)
6.4.5 Experiment 2: Automatic Extraction of Discriminating fNIRs Features
223(1)
6.4.6 Experiment 3: Optimal Parameter Setting of Feature Selection and Classifier Units
223(3)
6.5 Biological Implications
226(1)
6.6 Performance Analysis
226(6)
6.6.1 Performance Analysis of the Proposed IT2FS and GT2FS Classifier
226(3)
6.6.2 Statistical Validation of the Classifier Using McNemar's Test
229(3)
6.7 Conclusions
232(1)
References
232(7)
7 Conclusions and Future Directions of Research on BCI-Based Memory and Learning 239(8)
7.1 Self-Review of the Works Undertaken in the Book
239(3)
7.2 Limitations of EEG BCI-Based Memory Experiments
242(1)
7.3 Further Scope of Future Research on Memory and Learning
242(3)
References
245(2)
Index 247
LIDIA GHOSH, PHD, is currently a post-doctoral research fellow on Brain Science and Memory Research, granted by Liverpool Hope University to Jadavpur University, India.

AMIT KONAR, PHD, is currently a Professor in the dept. of Electronics and Tele-Communication Engineering (ETCE), Jadavpur University. He is an author of 15 books including a Wiley title: Emotion Recognition-A Pattern Analysis Approach.

PRATYUSHA RAKSHIT, PHD, is an Assistant Professor of ETCE dept., Jadavpur University, India and is currently on lien to Basque Centre for Applied Mathematics, Bilbao, Spain.
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