Feature Engineering#

Estimated reading time:2 minutes

Extract band powers from real nm000118 SSVEP windows, compare independent and shared spectral preprocessing, and fit a scikit-learn baseline. Run tutorial 40 before 42 with the same EEGDASH_CACHE_DIR: 40 writes plot_40_features.csv and its JSON column schema, which 42 reads directly. Three participants require about 21 MB of signal files. These examples demonstrate a workflow, not a population benchmark.

Extract features from real EEG trials

Extract features from real EEG trials

Share spectral preprocessing with a feature tree

Share spectral preprocessing with a feature tree

Fit scikit-learn to the saved real feature table

Fit scikit-learn to the saved real feature table