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Computer Science
Human-Computer Interaction (HCI)
Brain-Computer Interfaces
1. Introduction to Brain-Computer Interfaces
2. Foundational Neuroscience for BCIs
3. Signal Acquisition Methods
4. Signal Processing Pipeline
5. Machine Learning and Translation
6. BCI Control Paradigms
7. Applications of BCI Technology
8. System Design and Evaluation
9. Challenges and Future Directions
Machine Learning and Translation
Supervised Learning Algorithms
Linear Methods
Linear Discriminant Analysis
Fisher's Linear Discriminant
Regularization Techniques
Multi-Class Extensions
Logistic Regression
Maximum Likelihood Estimation
Regularization
Multi-Class Classification
Non-Linear Methods
Support Vector Machines
Kernel Functions
Margin Maximization
Multi-Class SVM
K-Nearest Neighbors
Distance Metrics
Neighborhood Selection
Weighted Voting
Decision Trees
Splitting Criteria
Pruning Techniques
Random Forests
Neural Networks
Multi-Layer Perceptrons
Architecture Design
Backpropagation
Activation Functions
Convolutional Neural Networks
Convolution Layers
Pooling Layers
Feature Maps
Recurrent Neural Networks
Long Short-Term Memory
Gated Recurrent Units
Sequence Processing
Model Training and Validation
Training Data Preparation
Data Splitting
Training Set
Validation Set
Test Set
Data Augmentation
Synthetic Data Generation
Noise Addition
Temporal Shifts
Cross-Validation Techniques
K-Fold Cross-Validation
Leave-One-Out Cross-Validation
Stratified Cross-Validation
Time Series Cross-Validation
Performance Evaluation
Classification Metrics
Accuracy
Precision and Recall
F1-Score
Confusion Matrix
Regression Metrics
Mean Squared Error
Mean Absolute Error
R-Squared
Overfitting Prevention
Regularization Techniques
L1 Regularization
L2 Regularization
Elastic Net
Early Stopping
Dropout
Unsupervised Learning
Clustering Algorithms
K-Means Clustering
Centroid Initialization
Convergence Criteria
Cluster Validation
Hierarchical Clustering
Agglomerative Methods
Divisive Methods
Dendrogram Analysis
Density-Based Clustering
DBSCAN
Mean Shift
Gaussian Mixture Models
Dimensionality Reduction
Principal Component Analysis
Variance Maximization
Eigenvalue Problems
Reconstruction Error
t-Distributed Stochastic Neighbor Embedding
Probability Distributions
Gradient Descent
Perplexity Parameter
Uniform Manifold Approximation
Topological Structure
Local Connectivity
Global Structure
Reinforcement Learning for BCI Control
Fundamentals
Agent-Environment Interaction
Reward Signals
Policy Learning
Value-Based Methods
Q-Learning
Deep Q-Networks
Temporal Difference Learning
Policy-Based Methods
Policy Gradient Methods
Actor-Critic Methods
Trust Region Methods
BCI-Specific Applications
Cursor Control
Robotic Control
Adaptive Interfaces
Co-adaptive Learning
Mutual Adaptation Principles
User Learning
Machine Learning
Feedback Loops
Online Learning Algorithms
Incremental Learning
Adaptive Filtering
Concept Drift Handling
User Feedback Integration
Explicit Feedback
Implicit Feedback
Error-Related Potentials
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4. Signal Processing Pipeline
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6. BCI Control Paradigms