eegdash.features.utils#

Feature Extraction Utilities.

This module provides the primary entry points for applying feature extraction pipelines to windowed datasets.

The module provides the following functions:

  • extract_features() — The main interface for computing features across an entire concatenated dataset.

  • fit_feature_extractors() — Fits trainable features using a representative dataset.

Functions

extract_features(concat_dataset, features, *)

Extract features from a collection of windowed recordings.

fit_feature_extractors(concat_dataset, features)

Fit trainable feature extractors on a concatenated dataset.
eegdash.features.utils.extract_features(concat_dataset: BaseConcatDataset, features: FeatureExtractor | Dict[Any, Callable] | List[Callable], *, batch_size: int = 512, n_jobs: int = 1) → FeaturesConcatDataset[source]

Extract features from a collection of windowed recordings.

This function applies a feature extraction pipeline to every individual recording in a BaseConcatDataset.

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 used for feature extraction within each recording.

  • n_jobs (int, default 1) – The number of parallel jobs to use for processing different recordings simultaneously.

Returns:

A unified collection of feature datasets corresponding to the input recordings.

Return type:

FeaturesConcatDataset

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

Fit trainable feature extractors on a concatenated dataset.

Scans the provided feature pipeline for components that require training (subclasses of TrainableFeature). If found, the function iterates through the dataset in batches to perform partial fitting before finalization.

Parameters:

  • concat_dataset (BaseConcatDataset) – The dataset used to train the feature extractors.

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

  • batch_size (int, default 8192) – The batch size to use when streaming data through the partial_fit() phase.

Returns:

The fitted feature extractor instance, ready for feature extraction.

Return type:

FeatureExtractor

Notes

If the provided extractors are not trainable, the function returns the original input without modification.