7 Sections
32 Lessons
8 Weeks
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Section 1 — EEG & MNE Fundamentals
4
1.1
Module 1 — Introduction to EEG & MNE | Week 1 Build a foundation in EEG data analysis by understanding EEG basics, brain waves, and the MNE environment. (Copy)
1.2
Main Topics: EEG Basics • Brain Waves • MNE Setup (Copy)
1.3
Practical: Load Sample EEG Data (Copy)
1.4
Output: Dataset Information Report (Copy)
Section 2 — EEG Data Exploration
4
2.1
Module 2 — Data Loading & Visualization | Week 2 Learn how to load and visually inspect EEG recordings and understand their structure. (Copy)
2.2
Main Topics: EDF/FIF • Montage • EEG Plotting (Copy)
2.3
Practical: Plot Raw EEG, PSD & Sensors (Copy)
2.4
Output: EEG Visualization Report (Copy)
Section 3 — EEG Signal Preprocessing
8
3.1
Module 3 — Preprocessing | Week 3 Learn essential techniques for preparing raw EEG signals for further analysis. (Copy)
3.2
Main Topics: Filtering • Notch Filtering • Bad Channels • Referencing (Copy)
3.3
Output: Cleaned EEG Report (Copy)
3.4
Practical: Clean EEG Signal (Copy)
3.5
Output: Preprocessing Comparison (Copy)
3.6
Module 4 — Artifact Removal | Week 4 Identify and remove unwanted components from EEG recordings using ICA-based workflows. (Copy)
3.7
Main Topics: ICA • Eye-Blink Artifacts • Muscle Artifacts (Copy)
3.8
Practical: Remove Artifacts Using ICA (Copy)
Section 4 — ERP & Event Analysis
4
4.1
Module 5 — Epoching & ERP | Week 5 Learn how EEG recordings can be segmented around events and analyzed through evoked responses. (Copy)
4.2
Main Topics: Events • Epochs • Baseline • Evoked Response (Copy)
4.3
Practical: Create Epochs & ERP Plots (Copy)
4.4
Output: ERP Analysis (Copy)
Section 5 — Frequency Analysis
4
5.1
Module 6 — Time-Frequency Analysis | Week 6 Explore EEG activity across different frequency ranges and analyze changes over time. (Copy)
5.2
Main Topics: PSD • Band Power • Morlet • ERD/ERS (Copy)
5.3
Practical: Analyze Frequency Bands (Copy)
5.4
Output: Band Power Report (Copy)
Section 6 — Machine Learning for EEG
4
6.1
Output: ML Performance Report (Copy)
6.2
Practical: EEG Classification (Copy)
6.3
Main Topics: CSP • SVM • Random Forest • XGBoost (Copy)
6.4
Module 7 — Feature Extraction & ML | Week 7 Introduce feature extraction and machine-learning methods for EEG classification. (Copy)
Section 7 — Final EEG Project
4
7.1
Output: Final Presentation (Copy)
7.2
Practical: Complete Project (Copy)
7.3
Main Topics: BCI • Motor Imagery (MI) • Source Basics • Connectivity Basics (Copy)
7.4
Module 8 — Final Project | Week 8 Bring together the concepts covered throughout the course in a complete EEG analysis project. (Copy)
EEG Data with MNE
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