meegkit.lof#

Local Outlier Factor (LOF).

Functions

LOF([n_neighbors, metric, threshold])

Local Outlier Factor.
class meegkit.lof.LOF(n_neighbors=20, metric='euclidean', threshold=1.5, **kwargs)#

Bases: object

Local Outlier Factor.

Local Outlier Factor (LOF) is an automatic, density-based outlier detection algorithm based on [1] and [2].

Parameters:

  • n_neighbours (int) – Number of neighbours defining the local neighbourhood.

  • metric (str in {'euclidean', 'nan_euclidean', 'cosine',) – ‘cityblock’, ‘manhattan’} Metric to use for distance computation. Default is “euclidean”

  • threshold (float) – Threshold to define outliers. Theoretical threshold ranges anywhere between 1.0 and any integer. Default: 1.5

Notes

It is recommended to perform a CV (e.g., 10-fold) on training set to calibrate this parameter for the given M/EEG dataset.

See [2] for details.

References

[1]

Breunig M, Kriegel HP, Ng RT, Sander J. 2000. LOF: identifying density-based local outliers. SIGMOD Rec. 29, 2, 93-104. https://doi.org/10.1145/335191.335388

[2] (1,2)

Kumaravel VP, Buiatti M, Parise E, Farella E. 2022. Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF). Sensors (Basel). 2022 Sep 27;22(19):7314. doi: 10.3390/s22197314. PMID: 36236413; PMCID: PMC9571252.

__init__(n_neighbors=20, metric='euclidean', threshold=1.5, **kwargs)#
predict(X)#

Detect bad channels using Local Outlier Factor algorithm.

Parameters:

X (array, shape=(n_channels, n_samples)) – The data X should have been high-pass filtered.

Returns:

bad_channel_indices

Return type:

Detected bad channel indices.