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BadChannelsLof

Namespace: RTPROCESSINGLIB  ·  Library: DSP Library

Module

This page documents a header-level module — a collection of free functions that share an algorithmic topic. There is no enclosing C++ class; the functions live directly in the library namespace.

Python equivalent

mne.preprocessing._lof in MNE-Python.

#include <dsp/bad_channels_lof.h>

SPDX-License-Identifier: BSD-3-Clause Copyright (c) 2026 MNE-CPP Authors.

LOF (Breunig et al., SIGMOD 2000) scores each channel by the ratio between its own local density (reachability distance to its k nearest channels in the chosen feature space) and the average local density of those neighbours. Values close to 1 indicate a sample that belongs to a dense cluster, values noticeably above 1 mark density outliers. For MEG / EEG each channel is summarised by per-segment statistics (RMS, variance, higher-order moments) so that genuinely flat, very noisy or amplifier- saturated sensors stand out in the LOF score and can be flagged at a single threshold (default 2.0).

The detector follows MNE-Python's mne.preprocessing.find_bad_channels_lof so MEG / EEG pipelines can switch between the two without changing the downstream interpolation / SSS step.

Functions​

findBadChannelsLof(data, info, params)​

QStringList findBadChannelsLof(const Eigen::MatrixXd & data, const [FiffInfo](/docs/api/fiff/fiff-info) & info, const LofBadChannelParams & params);

Detect bad channels using Local Outlier Factor (LOF).

Computes features for each channel (standard deviation, kurtosis, max absolute amplitude) and applies the LOF algorithm to identify outlier channels. LOF compares the local density of a point's neighbourhood to the densities of its neighbours.

Parameters:

  • data : const Eigen::MatrixXd & Data matrix (n_channels x n_times).

  • info : const FiffInfo & FiffInfo with channel types.

  • params : const LofBadChannelParams & LOF parameters.

Returns:

  • QStringList — List of bad channel names.

computeLofScores(features, k)​

Eigen::VectorXd computeLofScores(const Eigen::MatrixXd & features, int k);

Compute LOF scores for a feature matrix.

Pure algorithmic LOF implementation operating on feature vectors.

Parameters:

  • features : const Eigen::MatrixXd & Feature matrix (n_points x n_features).

  • k : int Number of neighbours.

Returns:

  • Eigen::VectorXd — LOF score for each point (n_points x 1). Values > 1 indicate outliers.

Authors of this file​