Learn to apply machine learning to EEG data with our comprehensive free course!
Course Overview
This self-paced course teaches you everything needed to build ML-powered BCI applications:
Module 1: Foundations (Weeks 1-2)
- EEG signal fundamentals
- Python for signal processing
- NumPy and SciPy basics
- Visualization with Matplotlib
Module 2: Feature Engineering (Weeks 3-4)
- Time-domain features
- Frequency-domain features (FFT, PSD)
- Time-frequency analysis (wavelets, STFT)
- Spatial filters (CSP, ICA)
Module 3: Classical ML (Weeks 5-6)
- LDA and SVM classifiers
- Random forests
- Cross-validation strategies
- Hyperparameter tuning
Module 4: Deep Learning (Weeks 7-8)
- CNN architectures for EEG
- RNN and LSTM networks
- Attention mechanisms
- Transfer learning
Module 5: Applications (Weeks 9-10)
- Motor imagery classification
- P300 detection
- Sleep stage classification
- Real-time implementation
Course Format
- Video Lectures: 50+ hours of content
- Coding Exercises: Hands-on Jupyter notebooks
- Projects: 5 real-world applications
- Datasets: Curated EEG datasets included
- Certificate: Upon completion
Prerequisites
- Basic Python programming
- High school mathematics
- Curiosity and dedication!
Enrollment
Course is completely free and self-paced:
Instructors
Taught by experienced researchers and industry professionals with years of BCI development experience.
Community
Join study groups on Discord, attend live office hours, and collaborate with fellow students.
Start your ML for EEG journey today!
