Kfold

bciflow.modules.core.kfold.kfold(target: Dict[str, Any], start_window: float, start_test_window: float | None = None, pre_folding: Dict[str, tuple] | None = None, pos_folding: Dict[str, tuple] = {}, window_size: float = 1.0, source: list = None) → DataFrame[source]

This method is used to perform a stratified k-fold cross-validation. The method is designed to work with eegdata dictionary.

Parameters:

  • target (dict) – Input EEG data in the form of a dictionary. The dictionary should contain the following keys: - ‘X’: The EEG data as a numpy array. - ‘y’: The labels corresponding to the EEG data. - ‘sfreq’: The sampling frequency of the EEG data. - ‘y_dict’: A dictionary mapping the labels to integers. - ‘events’: A dictionary describing the event markers. - ‘ch_names’: A list of channel names. - ‘tmin’: The start time of the EEG data.

  • start_window (int) – The start time of the window to be used in the crop method of eegdata for the training set.

  • start_test_window (int) – The start time of the window to be used in the crop method of eegdata for the test set.

  • pre_folding (dict) – A dictionary containing the preprocessing functions to be applied to the data before the cross-validation. The keys are the names of the preprocessing functions, and the values are tuples containing the function and its parameters.

  • pos_folding (dict) – A dictionary containing the postprocessing functions to be applied to the data before the cross-validation. The keys are the names of the postprocessing functions, and the values are the functions. The ‘clf’ key is reserved for the classifier, and its value should be a tuple containing the classifier and its parameters.

  • window_size (float) – The size of the window to be used in the crop method of eegdata.

  • source (list) – List of Eeg data from anothers subjects to be used as a source for the Transfer Learning modules

Returns:

results – A pandas dataframe containing the results of the cross-validation. The columns are ‘fold’, ‘tmin’, ‘true_label’, and the labels of the events in the target object.

Return type:

pandas.DataFrame

Raises:

ValueError – If any of the input parameters are invalid

Example

Applying k-fold cross-validation on EEG data:

>>> from bciflow.modules.core.kfold import kfold
>>> import numpy as np
>>> target = {
    'X': np.random.rand(100, 64, 256),
    'y': np.random.randint(0, 2, size=100),
    'sfreq': 256,  # Sampling frequency
    'y_dict': {0: 'class_0', 1: 'class_1'},
    'events': {'event_1': [0, 50], 'event_2': [51, 100]},
    'ch_names': [f'ch_{i}' for i in range(64)],
    'tmin': -0.5
}
>>> start_window = 0.0
>>> start_test_window = 0.5
>>> results = kfold(target, start_window, start_test_window)
>>> print(results.head())  # Display the first few rows of the results