eegdash.features.serialization#

Serialization Utilities for Feature Datasets.

This module provides functions for reconstructing feature datasets from disk. It serves as the inverse of the saving logic implemented in FeaturesConcatDataset and FeatureExtractor, allowing for efficient, parallelized reloading of processed features and their associated metadata.

Functions

feature_extractor_from_dict(fe_dict)

Get a feature extractor from a dictionary.

load_feature_extractor_from_hocon(path)

Reads a feature extractor from a HOCON's conf file.

load_feature_extractor_from_json(path)

Reads a feature extractor from a json file.

load_feature_extractor_from_yaml(path)

Reads a feature extractor from a yaml file.

load_features_concat_dataset(path[, ...])

Load a stored FeaturesConcatDataset from a directory.
eegdash.features.serialization.feature_extractor_from_dict(fe_dict: dict) → eegdash.features.extractors.FeatureExtractor[source]

Get a feature extractor from a dictionary.

Get a feature extractor object from a dictionary saved by FeatureExtractor.to_dict().

Parameters:

fe_dict (dict) – A dictionary representing the feature extractor, with "feature_extractors" and "preprocessor" fields (if applicable).

Returns:

A feature extractor

Return type:

FeatureExtractor

See also

FeatureExtractor.to_dict

Notes

  • Only feature_bank features and preprocessors

    are supported.

  • Feature extractors including non-function callables are not supported.

eegdash.features.serialization.load_feature_extractor_from_hocon(path: str | Path) → eegdash.features.extractors.FeatureExtractor[source]

Reads a feature extractor from a HOCON’s conf file.

Parameters:

path (str | pathlib.Path) – The path to the conf file.

See also

FeatureExtractor.to_hocon, feature_extractor_from_dict

Notes

  • Only feature_bank features and

    preprocessors are supported.

  • Feature extractors including non-function callables are not

    supported.

  • Requires the pyhocon package.

eegdash.features.serialization.load_feature_extractor_from_json(path: str | Path) → eegdash.features.extractors.FeatureExtractor[source]

Reads a feature extractor from a json file.

Parameters:

path (str | pathlib.Path) – The path to the json file.

See also

FeatureExtractor.to_json, feature_extractor_from_dict

Notes

  • Only feature_bank features and

    preprocessors are supported.

  • Feature extractors including non-function callables are not

    supported.

eegdash.features.serialization.load_feature_extractor_from_yaml(path: str | Path) → eegdash.features.extractors.FeatureExtractor[source]

Reads a feature extractor from a yaml file.

Parameters:

path (str | pathlib.Path) – The path to the yaml file.

Notes

  • Only feature_bank features and

    preprocessors are supported.

  • Feature extractors including non-function callables are not

    supported.

  • Requires the yaml package.

See also

FeatureExtractor.to_yaml, feature_extractor_from_dict

eegdash.features.serialization.load_features_concat_dataset(path: str | Path, ids_to_load: list[int] | None = None, n_jobs: int = 1) → eegdash.features.datasets.FeaturesConcatDataset[source]

Load a stored FeaturesConcatDataset from a directory.

This function reconstructs a concatenated dataset by loading individual FeaturesDataset instances from numbered subdirectories.

Parameters:

  • path (str or pathlib.Path) – The root directory where the dataset was previously saved. This directory should contain numbered subdirectories.

  • ids_to_load (list of int, optional) – A list of specific recording IDs (subdirectory names) to load. If None, all numbered subdirectories found in the path are loaded in ascending numerical order.

  • n_jobs (int, default=1) – The number of CPU cores to use for parallel loading. Set to -1 to use all available processors.

Returns:

A unified concatenated dataset containing the loaded recordings.

Return type:

FeaturesConcatDataset

See also

braindecode.datautil.load_concat_dataset

Notes

The function expects the directory structure generated by FeaturesConcatDataset.save(). It automatically reconstructs the feature DataFrames (safetensors), metadata (Pickle), recording info (FIF), and preprocessing keyword arguments (JSON).