Embark on a Python-based machine learning project focused on classifying EEG time-series data from a sleep study. Learn to differentiate between awake and asleep states using 30-second EEG segments and random forest classification. Explore key concepts including the EDF data format, power spectral density, and the classification scheme. Follow along with a step-by-step guide covering code setup, data loading and pre-processing, feature engineering, and implementation of the random forest classifier. Gain practical insights into working with EEG data and applying machine learning techniques to real-world neuroscience problems.
Overview
Syllabus
- Introduction:
- Project Overview:
- EDF Data Format:
- Power Spectra Density:
- Classification Scheme:
- Code Setup:
- Loading Data and Pre-Processing:
- Feature Engineering:
- Classification with Random Forest:
- Conclusion:
Taught by
Yacine Mahdid
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