FBCSP-SVM Usage
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Preparation for FBCSP_SVM’s input
An example of FBCSP_SVM’s input on the BCIC IV 2a dataset (All settings are set up as optimal settings in the original paper).
Downloading a particular dataset
python download_datasets.py --dataset 'BCIC2a'
Preprocessing based on Filter Bank Common Spatial Pattern (FBCSP). over the considered dataset
python prep_FBCSP.py --dataset 'BCIC2a'
Build, fit, and evaluate FBCSP_SVM
# Subject-dependent MI classification
python run_FBCSP_SVM.py --model_name 'FBCSP_SVM' --dataset 'BCIC2a' --train_type 'subject_dependent' --data_type 'fbcsp' --num_class 2 --num_chs 20
# Subject-independent MI classification
python run_FBCSP_SVM.py --model_name 'FBCSP_SVM' --dataset 'BCIC2a' --train_type 'subject_independent' --data_type 'fbcsp' --num_class 2 --num_chs 20
Arguments:
| Arguments | Description | Default | |
|---|---|---|---|
| model_name | str prefix to save model | ‘FBCSP_SVM’ | |
| dataset | str prefix to pick up a particular dataset and save model | ‘BCIC2a’ | |
| train_type | str prefix to pick up a particular traning manner and save model | ‘subject_dependent’ | |
| data_type | str prefix to select data type of input | ‘time_domain’ | |
| num_class | int number of classes | 2 | |
| num_chs | int number of classes | 20 | |
| log_dir | str path to save model | ‘logs’ | |
| subjects | int or list of int or None list of range test subject, if None, use all subjects | None | - |