2.1 and 2.2 written

project_report.md

@@ -31,6 +31,7 @@ Purpose:
 - Keep relevant columns (FACE_AUs and eye-tracking raw values)
 - Filter invalid samples (e.g., invalid level segments): Make sure not to drop rows where NaN is necessary for later feature creation, therefore use subset argument in dropNa()! 
 - Export subject-level parquet files
+- Before running the scripts: be aware that the whole dataset contains 30 files with around 900 Mbytes each, provide enough storage and expect this to take a while.
 
 
 ### 2.2 Feature Engineering (Offline)
@@ -39,10 +40,14 @@ Main script:
 - `dataset_creation/combined_feature_creation.py`
 
 Behavior:
-- Builds fixed-size sliding windows over subject time series
+- Builds fixed-size sliding windows over subject time series (window size and step size can be adjusted)
+- Uses prepared parquet files from 2.1
 - Aggregates AU statistics per window (e.g., `FACE_AUxx_mean`)
 - Computes eye-feature aggregates (fix/sacc/blink/pupil metrics)
-- Produces training-ready feature tables
+- Produces training-ready feature tables = dataset
+- Parameter ```MIN_DUR_BLINKS``` can be adjusted, although this value needs to make sense in combination with your sampling frequency
+- With low videostream rates, consider to reevaluate the meaningfulness of some eye-tracking features, especially the fixations
+- running the script requires a manual installation of pygaze Analyser library from [github](https://github.com/esdalmaijer/PyGazeAnalyser.git)
 
 ### 2.3 Online Camera + Eye + AU Feature Extraction