adding a bad channel detection method using LOF algorithm

mne/preprocessing/detect_bad_channels.py

@@ -0,0 +1,67 @@
+# Authors: Velu Prabhakar Kumaravel <vkumaravel@fbk.eu>      
+# License: BSD-3-Clause
+
+
+import numpy as np
+
+from ..io.base import BaseRaw
+from .. import Transform
+from ..utils import (logger, verbose, _validate_type)
+
+
+@verbose
+def mark_bad_channels_lof(raw, n_neighbors = 8, metric = 'sqeuclidean', lof_threshold = 1.5):
+    """ Detects and marks bad channels using Local Outlier Factor (LOF) algorithm.
+
+    Detects bad (a.k.a. anamolous) channels using a local density based algorithm:LOF.
+    By default, n_neighbors parameter is set to 8, which will work in most cases
+
+    See :footcite:`kumaravel2022` for background on choosing
+    ``metric`` and ``lof_threshold``.
+
+    Parameters
+    ----------
+    raw : instance of Raw
+        The raw data.
+
+    n_neighbors : int
+        Number of neighbors for computing local density (default: 8).
+
+    metric : 'euclidean' | 'sqeuclidean' | 'cityblock' | 'cosine' | 'l1' | 'l2' | 'manhattan' | 'nan_euclidean'
+        The metric used for distance compuation. Default: 'sqeuclidean' as it performed better than 'euclidean' in MATLAB implementation.
+        One could consider this as an hyperparameter to optimize.
+
+    lof_threshold : float
+        Decision threshold for outlier/bad channels. Default: 1.5. If you deal with noisier data like newborns EEG, you might use a higher value (e.g., 2.5)
+        One could consider this as an hyperparameter to optimize.
+    %(n_jobs)s
+    %(verbose)s
+
+    Returns
+    -------
+    raw : instance of Raw
+        The raw instance with updated 'info'
+
+    References
+    ----------
+    footbibliography: Kumaravel et al. 2022. "Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF)" Sensors 22, no. 19: 7314. https://doi.org/10.3390/s22197314
+    """
+
+    from sklearn.neighbors import LocalOutlierFactor
+
+    _validate_type(raw, BaseRaw, 'raw')
+    raw_copy = raw.copy()
+
+    clf = LocalOutlierFactor(n_neighbors)
+    data = raw_copy.get_data()
+    clf.fit_predict(data)
+    lof_scores = clf.negative_outlier_factor_
+    bad_channel_indices = [i for i,v in enumerate(np.abs(lof_scores)) if v >= lof_threshold]
+
+    for elem in bad_channel_indices:
+        logger.info('LOF: Marking channel %s as bad'
+                % raw_copy.info['chs'][elem]["ch_name"])
+        raw_copy.info['bads'].append(raw_copy.info['chs'][elem]["ch_name"])
+
+
+    return raw_copy