eegdash.features.utils module#

eegdash.features.utils.extract_features(concat_dataset: BaseConcatDataset, features: FeatureExtractor | Dict[str, Callable] | List[Callable], *, batch_size: int = 512, n_jobs: int = 1) → FeaturesConcatDataset[source]

Extract features from a concatenated dataset of windows.

This function applies a feature extractor to each WindowsDataset within a BaseConcatDataset in parallel and returns a FeaturesConcatDataset with the results.

Parameters:

  • concat_dataset (BaseConcatDataset) – A concatenated dataset of WindowsDataset or EEGWindowsDataset instances.

  • features (FeatureExtractor or dict or list) – The feature extractor(s) to apply. Can be a FeatureExtractor instance, a dictionary of named feature functions, or a list of feature functions.

  • batch_size (int, default 512) – The size of batches to use for feature extraction.

  • n_jobs (int, default 1) – The number of parallel jobs to use for extracting features from the datasets.

Returns:

A new concatenated dataset containing the extracted features.

Return type:

FeaturesConcatDataset

eegdash.features.utils.fit_feature_extractors(concat_dataset: BaseConcatDataset, features: FeatureExtractor | Dict[str, Callable] | List[Callable], batch_size: int = 8192) → FeatureExtractor[source]

Fit trainable feature extractors on a dataset.

If the provided feature extractor (or any of its sub-extractors) is trainable (i.e., subclasses TrainableFeature), this function iterates through the dataset to fit it.

Parameters:

  • concat_dataset (BaseConcatDataset) – The dataset to use for fitting the feature extractors.

  • features (FeatureExtractor or dict or list) – The feature extractor(s) to fit.

  • batch_size (int, default 8192) – The batch size to use when iterating through the dataset for fitting.

Returns:

The fitted feature extractor.

Return type:

FeatureExtractor