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.