Chapter 1: Introduction to Human Memory and Learning Models -- 1.1 Introduction 2 -- 1.2 Philosophical Contributions to Memory Research 4 -- 1.2.1 Atkinson and Shiffrin's Model 4 -- 1.2.2 Tveter's Model 6 -- 1.2.3 Tulving's model 6 -- 1.2.4 The Parallel and Distributed Processing (PDP) Approach 8 -- 1.2.5 Procedural and Declarative Memory 9 -- 1.3 Brain-theoretic Interpretation of Memory Formation 11 -- 1.3.1 Coding for Memory 11 -- 1.3.2 Memory Consolidation 13 -- 1.3.3 Location of stored Memories 16 -- 1.3.4 Isolation of Information in Memory 16 -- 1.4 Cognitive Maps 17 -- 1.5 Neural Plasticity 18 -- 1.6 Modularity 19 -- 1.7 The cellular Process behind STM Formation 20 -- 1.8 LTM Formation 21 -- 1.9 Brain Signal Analysis in the Context of Memory and Learning 22 -- 1.9.1 Association of EEG alpha and theta band with memory performances 22 -- 1.9.2 Oscillatory beta and gamma frequency band activation in STM performance 26 -- 1.9.3 Change in EEG band power with changing working memory load 26 -- 1.9.4 Effects of Electromagnetic field on the EEG response of Working Memory 29 -- 1.9.5 EEG Analysis to discriminate focused attention and WM performance 30 -- 1.9.6 EEG power changes in memory repetition effect 31 -- 1.9.7 Correlation between LTM Retrieval and EEG features 34 -- 1.9.8 Impact of math anxiety on WM response: An EEG study 37 -- 1.10 Memory Modelling by Computational Intelligence Techniques 38 -- 1.11 Scope of the Book 43 -- References 47 -- Chapter 2: Working Memory Modeling Using Inverse Fuzzy Relational Approach -- 2.1 Introduction 56 -- 2.2 Problem Formulation and Approach 59 -- 2.2.1 Independent Component Analysis as a Source Localization Tool 61 -- 2.2.2 Independent Component Analysis vs Principal Component Analysis 62 -- 2.2.3 Feature Extraction 63 -- 2.2.4 Phase 1: WM Modeling 64 -- 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 65 -- 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 68.

2.2.5 Phase 2: WM Analysis 69 -- 2.2.6 Finding Max-Min Compositional of Weight Matrix 70 -- 2.3 Experimental Results and Performance Analysis 75 -- 2.3.1 Experimental Set-up 75 -- 2.3.2 Source Localization using e-LORETA 78 -- 2.3.3 Pre-processing 79 -- 2.3.4 Selection of EEG Features 80 -- 2.3.5 WM Model Consistency across Partial Face Stimuli 81 -- 2.3.6 Inter-person Variability of Weight Matrix W 85 -- 2.3.7 Variation in Imaging Attributes 87 -- 2.3.8 Comparative Analysis with existing Fuzzy Inverse Relations 87 -- 2.4 Discussion 88 -- 2.5 Conclusion 89 -- References 90 -- Chapter 3: Short-Term Memory Modeling in Shape-Recognition Task by Type-2 Fuzzy Deep Brain Learning -- 3.1 Introduction 98 -- 3.2 System Overview 101 -- 3.3 Brain Functional Mapping using Type-2 Fuzzy DBLN 107 -- 3.3.1 Overview of Type-2 Fuzzy Sets 107 -- 3.3.2 Type-2 Fuzzy Mapping and Parameter Adaptation by Perceptron-like Learning 108 -- 3.3.2.1 Construction of the Proposed Interval Type-2 Fuzzy Membership Function 109 -- 3.3.2.2 Construction of IT2FS Induced Mapping Function 110 -- 3.3.2.3 Secondary Membership Function Computation of Proposed GT2FS 112 -- 3.3.2.4 Proposed General Type-2 Fuzzy Mapping 114 -- 3.3.3 Perceptron-like Learning for Weight Adaptation 115 -- 3.3.4 Training of the Proposed Shape-Reconstruction Algorithm 116 -- 3.3.5 The Test Phase of the Memory Model 118 -- 3.4 Experiments and Results 118 -- 3.4.1 Experimental Set-up 118 -- 3.4.2 Experiment 1: Validation of the STM Model with respect to Error Metric 121 -- 3.4.3 Experiment 2: Similar Encoding by a Subject for Similar Input Object-Shapes 122 -- 3.4.4 Experiment 3: Study of Subjects' Learning Ability with Increasing Complexity in Object Shape 123 -- 3.4.5 Experiment 4: Convergence Time of the Weight Matrix G for Increased Complexity of the Input Shape Stimuli 124 -- 3.4.6 Experiment 5: Abnormality in G matrix for the subjects with Brain Impairment 125 -- 3.5 Biological Implications 126 -- 3.6 Performance Analysis 128.

4.5.5 Statistical Validation using Wilcoxon Signed-Rank Test 172 -- 4.6 Conclusion 173 -- References 173 -- Chapter 5: EEG Analysis to Decode Human Memory Responses in Face Recognition Task Using Deep LSTM Network -- 5.1 Introduction 182 -- 5.2 CSP Modeling 186 -- 5.2.1 The Standard CSP Algorithm 186 -- 5.2.2 The Proposed CSP Algorithm 187 -- 5.3 Proposed LSTM Classifier with Attention Mechanism 189 -- 5.4 Experiment and Results 195 -- 5.4.1 The Experimental Set-up 195 -- 5.4.2 Experiment 1: Activated Brain Region Selection using eLORETA 196 -- 5.4.3 Experiment 2: Detection of the ERP signals associated with the familiar andunfamiliar face discrimination 198 -- 5.4.4 Experiment 3: Performance Analysis of the Proposed CSP algorithm as a Feature extraction Technique 199 -- 5.4.5 Experiment 4: Performance Analysis of the Proposed LSTM based Classifier 201 -- 5.4.6 Experiment 5: Classifier Performance Analysis with varying EEG Time-Window Length 202 -- 5.4.7 Statistical Validation of the Proposed LSTM Classifier using McNamers' Test 203 -- 5.5 Conclusions 204 -- References 204 -- Chapter 6: Cognitive Load Assessment in Motor Learning Tasks by Near-Infrared Spectroscopy Using Type-2 Fuzzy Sets -- 6.1 Introduction 214 -- 6.2 Principles and Methodologies 216 -- 6.2.1 Normalization of Raw Data 217 -- 6.2.2 Pre-processing 218 -- 6.2.3 Feature Extraction 218 -- 6.2.4 Training Instance Generation for Offline Training 219 -- 6.2.5 Feature Selection using Evolutionary Algorithm 219 -- 6.2.6 Classifier Training and Testing 221 -- 6.3 Classifier Design 221 -- 6.3.1 Preliminaries of IT2FS and GT2FS 221 -- 6.3.2 IT2FS Induced Classifier Design 222 -- 6.3.3 GT2FS Induced Classifier Design 228 -- 6.4 Experiments and Results 230 -- 6.4.1 Experimental Set-up 230 -- 6.4.2 Participants 232 -- 6.4.3 Stimulus Presentation for Online Classification 232 -- 6.4.4 Experiment 1: Demonstration of decreasing Cognitive Load with increasing Learning Epochs for similar stimulus 233 -- 6 .4.5 Experiment 2: Automatic Extraction of Discriminating fNIRs features 234.

6.4.6 Experiment 3: Optimal Parameter Setting of Feature Selection and Classifier Units 235 -- 6.5 Biological Implications 237 -- 6.6 Performance Analysis 239 -- 6.6.1 Performance Analysis of the proposed IT2FS and GT2FS Classifier 239 -- 6.6.2 Statistical Validation of the Classifiers using McNamer;s Test 242 -- 6.7 Conclusion 243 -- References 243 -- Chapter 7: Conclusions and Future Directions of Research on BCI based Memory and Learning -- 7.1 Self-Review of the Works Undertaken in the Book 250 -- 7.2 Limitations of EEG BCI-Based Memory Experiments 252 -- 7.3 Further Scope of Future Research on Memory and Learning 253 -- References.