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Towards Non-Invasive Hybrid Brain-Computer Interface (hBCIs)
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  • Brain-Heart Tech & Beyond
  • 1. Introduction
    • 1. Motivation and Problem Statement
  • 2. Overview of Applications in Brain-Heart Dynamics
    • 2.1. Intro
    • 2.2. Enhanced Hybrid Brain-Computer Interfaces
    • 2.3. Emotion Recognition
    • 2.4. Neurofeedback and Heart Rhythm (Biofeedback) Regulation
      • 2.4.1. Sleep, Performance Enhancement, and Cognitive Health
      • 2.4.2. Stress Management, Creativity, and Clinical Applications
    • 2.5. Personalized Health Monitoring and Adaptation
    • 2.6. Neurocardiology: Cardiovascular and Neurological Disorders
    • 2.7. Adaptive Gaming and VR Experience
    • 2.8. Outro
  • 3. Overview of Models and Techniques for Emotion Recognition
    • 3.1. Datasets
    • 3.2. Existing Models and Techniques for Emotion Recognition
    • 3.3. Machine Learning Models
      • 3.3.1. Decision Trees
      • 3.3.2. Random Forest
      • 3.3.3. Light Gradient Boosting Machine (LightGBM)
      • 3.3.4. K-Nearest Neighbors
      • 3.3.5. Support Vector Machines (SVM)
      • 3.3.6. Naive Bayes
    • 3.4. Deep Learning Models
      • 3.4.1. Convolutional Neural Networks
      • 3.4.2. Long Short-Term Memory Networks (LSTM)
      • 3.4.3. Temporal Convolutional Networks
      • 3.4.4. Attention Networks
      • 3.4.5. Transformer Models
      • 3.4.6. Graph Neural Networks
  • 3.5. Generative Models and Data Augmentation
    • 3.5.1. Variational Autoencoders
    • 3.5.2. Generative Adversarial Networks
  • 3.6. Other Architectures
  • 4. Stress Detection Model with EEG-ECG
    • 4.1. Introduction
    • 4.2. Dataset Overview
    • 4.3. Data Processing and System Design
    • 4.4. Data Loading and Parsing
    • 4.5. EEG Preprocessing and Feature Extraction
    • 4.6. ECG Preprocessing and Feature Extraction
    • 4.7. Merge ECG and EEG Features for Stress Detection
    • 4.8. Model Development
    • 4.9. Results and Evaluation
  • 5. Appendix & Bibliography
    • Appendix A. Overview of Methods and Results for Emotion Recognition Studies
    • Appendix B. Visualization of Mean Power Spectral Density (PSD) Across EEG Channels
    • Appendix C. Distribution of Emotion Dimension Ratings (Valence, Arousal, Dominance) Across Target
    • Bibliography
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3.5. Generative Models and Data Augmentation

3.5.1. Variational Autoencoders3.5.2. Generative Adversarial Networks
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