EEG Data with MNE
Description
EEG Data Analysis with MNE
Learn how to process, visualize, clean, analyze, and classify EEG (Electroencephalography) data using MNE through a structured, practical course.
The program introduces EEG fundamentals and progresses through data visualization, signal preprocessing, artifact removal, epoching, ERP analysis, time-frequency analysis, feature extraction, and machine learning.
Students gain hands-on experience working with EEG datasets and complete practical reports throughout the course, culminating in a final project covering BCI, Motor Imagery (MI), and introductory source/connectivity concepts.
What You’ll Learn
✓ Understand EEG fundamentals and brain-wave concepts.
✓ Set up MNE and work with EEG datasets.
✓ Load EEG data using EDF and FIF formats.
✓ Visualize raw EEG signals, sensors, montages, and PSD.
✓ Apply filtering, notch filtering, referencing, and bad-channel handling.
✓ Identify and remove eye-blink and muscle artifacts using ICA.
✓ Create events, epochs, baselines, and evoked responses.
✓ Perform ERP (Event-Related Potential) analysis.
✓ Analyze frequency bands using PSD, band power, Morlet, and ERD/ERS.
✓ Extract EEG features and perform classification using CSP, SVM, Random Forest, and XGBoost.
✓ Explore introductory BCI, Motor Imagery, source, and connectivity concepts.
- 8-Week Structured Curriculum
- Practical EEG Data Analysis
- MNE-Based Workflow
- EEG Signal Preprocessing
- ICA Artifact Removal
- ERP Analysis
- Time-Frequency Analysis
- Machine Learning for EEG
- BCI & Motor Imagery Basics
- Final Practical Project
- Neuroscience Students
- Biomedical Engineering Students
- Data Science Students
- AI & Machine Learning Learners
- EEG Research Beginners
- BCI Learners
- Research Scholars
- Students interested in brain-signal analysis
- The provided curriculum does not specify formal prerequisites. It begins with EEG basics and MNE setup before progressing into signal analysis and machine learning.
- 7 Sections
- 32 Lessons
- 8 Weeks
- Section 1 — EEG & MNE Fundamentals4
- 1.1Module 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)
- 1.2Main Topics: EEG Basics • Brain Waves • MNE Setup (Copy)
- 1.3Practical: Load Sample EEG Data (Copy)
- 1.4Output: Dataset Information Report (Copy)
- Section 2 — EEG Data Exploration4
- Section 3 — EEG Signal Preprocessing8
- 3.1Module 3 — Preprocessing | Week 3 Learn essential techniques for preparing raw EEG signals for further analysis. (Copy)
- 3.2Main Topics: Filtering • Notch Filtering • Bad Channels • Referencing (Copy)
- 3.3Output: Cleaned EEG Report (Copy)
- 3.4Practical: Clean EEG Signal (Copy)
- 3.5Output: Preprocessing Comparison (Copy)
- 3.6Module 4 — Artifact Removal | Week 4 Identify and remove unwanted components from EEG recordings using ICA-based workflows. (Copy)
- 3.7Main Topics: ICA • Eye-Blink Artifacts • Muscle Artifacts (Copy)
- 3.8Practical: Remove Artifacts Using ICA (Copy)
- Section 4 — ERP & Event Analysis4
- Section 5 — Frequency Analysis4
- Section 6 — Machine Learning for EEG4
- Section 7 — Final EEG Project4
- 7.1Output: Final Presentation (Copy)
- 7.2Practical: Complete Project (Copy)
- 7.3Main Topics: BCI • Motor Imagery (MI) • Source Basics • Connectivity Basics (Copy)
- 7.4Module 8 — Final Project | Week 8 Bring together the concepts covered throughout the course in a complete EEG analysis project. (Copy)
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The course gave me a structured understanding of EEG data analysis, from loading and preprocessing signals to artifact removal, ERP analysis, frequency analysis, and machine learning. The weekly practical work made it easier to understand how a complete EEG analysis workflow comes together.
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