Dive into the world of deep learning for EEG signal processing with our comprehensive tutorial series!
What You'll Learn
This tutorial series covers everything from basics to advanced deep learning techniques for EEG analysis:
Part 1: Fundamentals
- Understanding EEG data structure
- Preprocessing and normalization
- Feature extraction basics
- Train/test split strategies
Part 2: Building Your First Model
- Setting up Keras and TensorFlow
- Creating a simple CNN for EEG classification
- Training and validation
- Model evaluation metrics
Part 3: Advanced Architectures
- LSTM networks for temporal dynamics
- Attention mechanisms
- Hybrid CNN-LSTM models
- Transfer learning with pre-trained models
Part 4: Real-World Applications
- Motor imagery classification
- Sleep stage detection
- Seizure prediction
- Emotion recognition
Prerequisites
- Python 3.8+
- Basic understanding of neural networks
- PiEEG device (or use our sample datasets)
- Familiarity with NumPy and Pandas
Tutorial Resources
All code and datasets are available on our GitHub:
- Jupyter notebooks with step-by-step explanations
- Pre-trained models for quick experimentation
- Sample EEG datasets
- Utility functions and helper libraries
Performance Results
Using our tutorial methods, you can achieve:
- Motor Imagery: 85-92% accuracy
- Sleep Staging: 88-94% accuracy
- P300 Detection: 90-95% accuracy
Join the Workshop
We're hosting free online workshops where you can learn alongside other researchers:
- Next Workshop: August 15, 2025
- Duration: 3 hours
- Format: Live coding session
- Registration: pieeg.com/workshops
