Random Forest and SVM classifiers now usе a CPU

data folder - store raw EEG data in .txt format

1. Run main_preparation.py inside DataPreparation folder, it creates file with processed raw data for classifier fitting
2. Run RandomForest_main.py inside RandomForest folder, it fit classifier and save it inside models folder

Classes: 0 = кнопка не нажата 1 = кнопка нажата левой 2 = кнопка нажата правой

CV_RandomForest.py Parameters obtained using a cross-validation parameters grid search.

Mark's dataset (whole dataset was used)

3 folds for cross-validation were used and n_iter=20

Best Params: {"n_estimators": 2000, "min_samples_split": 2, "min_samples_leaf": 2, "max_features": "auto", "max_depth": 50, "bootstrap": false} Best Accuracy: 0.9799830842965886

Kate's dataset (data = data.loc[500:150000])

3 folds for cross-validation were used and n_iter=7

Best Params: {'n_estimators': 1000, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 70, 'bootstrap': False} Best Accuracy: 0.9806521739130435

Plots are stored in Plots folder

1. Average precision score plot
2. Extension of Precision-Recall curve to multi-class plot

Color - Class For Extension of Precision-Recall curve to multi-class plot

• Class 0 - Navy color
• Class 1 - Darkorange color
• Class 2 - Green color
• Average precision score line - Gold color

• Set BASE_DIR in config.py

• Check which model is imported (variable: model)
• Check data imported for prediction (variable: data)
• Check channels on which classes are predicted (variable: channels). To get channels with highest variance - run variance.py inside Channel_selection folder

1. KNn - have GPU implementation, but now the CPU is used.

   TODO: implement GPU learning

2. CatBoost_Gradient - have GPU implementation.

   TODO: implement grid-search for parameters tuning

3. XGBoost - have GPU implementation.

   TODO: implement grid-search for parameters tuning

1. Frequency ranges for motor imagery registration: mu (8–12 Hz) and beta (13–28 Hz) [1]. Signal modulation is focused over sensorimotor cortex and in the alpha- and beta frequency bands associated with mu rhythm activity [2].

2. 

3. From the scalp EEG signals, it has also been found that imagination of movement leads to short-lasting and circumscribed attenuation (or accentuation) in mu (8–12 Hz) and beta (13–28 Hz) rhythmic activities, known as event-related desynchronization (or synchronization) (ERD/ERS) [1]. A person suppresses mu wave patterns when he or she performs a motor action or, with practice, when he or she visualizes performing a motor action [3].

P.S.: Mu waves are suppressed when mirror neurons fire. Mirror neurons play a role in mapping others' movements into the brain without actually physically performing the movements [3].

Sources:

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1989674/#R6

[2] https://www.bci2000.org/mediawiki/index.php/User_Tutorial:Introduction_to_the_Mu_Rhythm

[3] https://en.wikipedia.org/wiki/Mu_wave