🧠
Towards Non-Invasive Hybrid Brain-Computer Interface (hBCIs)
search
⌘Ctrlk
GithubGet in touch 📮
🧠
Towards Non-Invasive Hybrid Brain-Computer Interface (hBCIs)
  • rocket-launchTowards non-invasive hybrid Brain-Computer Interfaces (hBCI)
  • hand-wave1. Introduction
    • 1. Motivation and Problem Statement
  • brain-circuit2. 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
    • 2.5. Personalized Health Monitoring and Adaptation
    • 2.6. Neurocardiology: Cardiovascular and Neurological Disorders
    • 2.7. Adaptive Gaming and VR Experience
    • 2.8. Outro
  • gear-complex3. 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
    • 3.4. Deep Learning Models
  • 3.5. Generative Models and Data Augmentation
    • 3.5.1. Variational Autoencoders
    • 3.5.2. Generative Adversarial Networks
  • 3.6. Other Architectures
  • code-branch4. 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
    • line-heightAppendix A. Overview of Methods and Results for Emotion Recognition Studies
    • chart-lineAppendix B. Visualization of Mean Power Spectral Density (PSD) Across EEG Channels
    • chart-columnAppendix C. Distribution of Emotion Dimension Ratings (Valence, Arousal, Dominance) Across Target
    • landmark-magnifying-glassBibliography
gitbookPowered by GitBook
block-quoteOn this pagechevron-down

3.5. Generative Models and Data Augmentation

3.5.1. Variational Autoencoderschevron-right3.5.2. Generative Adversarial Networkschevron-right
Previous3.4.6. Graph Neural Networkschevron-leftNext3.5.1. Variational Autoencoderschevron-right