Cognitive modeling of human memory and learning a non-invasive brain-computer interfacing approach /

"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 sim...

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Bibliographic Details
Call Number:Libro Electrónico
Main Authors: Ghosh, Lidia (Author), Konar, Amit (Author), Rakshit, Pratyusha (Author)
Format: eBook
Language:Inglés
Published: Hoboken, New Jersey : Wiley, [2020]
Series:Wiley - IEEE Ser.
Subjects:
Memory.
Brain-computer interfaces.
Cognitive neuroscience.
Interfaces cerveau-ordinateur.
Neurosciences cognitives.
Brain-computer interfaces
Cognitive neuroscience
Memory
Online Access:Texto completo
Table of Contents:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Preface
  • Acknowledgments
  • About the Authors
  • Chapter 1 Introduction to Brain-Inspired Memory and Learning Models
  • 1.1 Introduction
  • 1.2 Philosophical Contributions to Memory Research
  • 1.2.1 Atkinson and Shiffrin's Model
  • 1.2.2 Tveter's Model
  • 1.2.3 Tulving's Model
  • 1.2.4 The Parallel and Distributed Processing (PDP) Approach
  • 1.2.5 Procedural and Declarative Memory
  • 1.3 Brain-Theoretic Interpretation of Memory Formation
  • 1.3.1 Coding for Memory
  • 1.3.2 Memory Consolidation
  • 1.3.3 Location of Stored Memories
  • 1.3.4 Isolation of Information in Memory
  • 1.4 Cognitive Maps
  • 1.5 Neural Plasticity
  • 1.6 Modularity
  • 1.7 The Cellular Process Behind STM Formation
  • 1.8 LTM Formation
  • 1.9 Brain Signal Analysis in the Context of Memory and Learning
  • 1.9.1 Association of EEG and Band with Memory Performances
  • 1.9.2 Oscillatory and Frequency Band Activation in STM Performance
  • 1.9.3 Change in EEG Band Power with Changing Working Memory Load
  • 1.9.4 Effects of Electromagnetic Field on the EEG Response of Working Memory
  • 1.9.5 EEG Analysis to Discriminate Focused Attention and WM Performance
  • 1.9.6 EEG Power Changes in Memory Repetition Effect
  • 1.9.7 Correlation Between LTM Retrieval and EEG Features
  • 1.9.8 Impact of Math Anxiety on WM Response: An EEG Study
  • 1.10 Memory Modeling by Computational Intelligence Techniques
  • 1.11 Scope of the Book
  • References
  • Chapter 2 Working Memory Modeling Using Inverse Fuzzy Relational Approach
  • 2.1 Introduction
  • 2.2 Problem Formulation and Approach
  • 2.2.1 Independent Component Analysis as a Source Localization Tool
  • 2.2.2 Independent Component Analysis vs. Principal Component Analysis
  • 2.2.3 Feature Extraction
  • 2.2.4 Phase 1: WM Modeling
  • 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
  • 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
  • 2.2.5 Phase 2: WM Analysis
  • 2.2.6 Finding Max-Min Compositional Inverse of Weight Matrix Wkc
  • 2.3 Experiments and Performance Analysis
  • 2.3.1 Experimental Set-up
  • 2.3.2 Source Localization Using eLORETA
  • 2.3.3 Pre-processing
  • 2.3.4 Selection of EEG Features
  • 2.3.5 WM Model Consistency Across Partial Face Stimuli
  • 2.3.6 Inter-person Variability of W
  • 2.3.7 Variation in Imaging Attributes
  • 2.3.8 Comparative Analysis with Existing Fuzzy Inverse Relations
  • 2.4 Discussion
  • 2.5 Conclusions
  • References
  • Chapter 3 Short-Term Memory Modeling in Shape-Recognition Task by Type-2 Fuzzy Deep Brain Learning
  • 3.1 Introduction
  • 3.2 System Overview
  • 3.3 Brain Functional Mapping Using Type-2 Fuzzy DBLN
  • 3.3.1 Overview of Type-2 Fuzzy Sets

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