Preprocessing#
Projections:
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Projection vector. |
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Compute SSP (signal-space projection) vectors on epoched data. |
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Compute SSP (signal-space projection) vectors on evoked data. |
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Compute SSP (signal-space projection) vectors on continuous data. |
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Read projections from a FIF file. |
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Write projections to a FIF file. |
Module dedicated to manipulation of channels.
Can be used for setting of sensor locations used for processing and plotting.
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Sensor layouts. |
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Montage for digitized electrode and headshape position data. |
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Compute the native-to-head transformation for a montage. |
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Fix magnetometer coil types. |
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Read Polhemus FastSCAN digitizer data from a |
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Get a list of all standard montages shipping with MNE-Python. |
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Make montage from arrays. |
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Read Polhemus digitizer data from a file. |
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Read electrode locations from CapTrak Brain Products system. |
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Read electrode positions from a |
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Read electrode locations from EGI system. |
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Read digitized points from a .fif file. |
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Read historical .hpts mne-c files. |
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Read Localite .csv file. |
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Read a generic (built-in) standard montage that ships with MNE-Python. |
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Read a montage from a file. |
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Compute device to head transform from a DigMontage. |
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Read layout from a file. |
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Choose a layout based on the channels in the info 'chs' field. |
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Create .lout file from EEG electrode digitization. |
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Generate .lout file for custom data, i.e., ICA sources. |
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Find the adjacency matrix for the given channels. |
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Get a list of all FieldTrip neighbor definitions shipping with MNE. |
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Read a channel adjacency ("neighbors") file that ships with MNE. |
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Equalize channel picks and ordering across multiple MNE-Python objects. |
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Rename channels. |
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Generate a custom 2D layout from xy points. |
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Return dict mapping from ROI names to lists of picks for 10/20 setups. |
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Combine channels based on specified channel grouping. |
Preprocessing with artifact detection, SSP, and ICA.
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Data decomposition using Independent Component Analysis (ICA). |
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Implementation of the Xdawn Algorithm. |
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Remove EOG artifact signals from other channels by regression. |
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Annotate raw data based on peak-to-peak amplitude. |
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Create |
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Detect segments with movement. |
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Create annotations for segments that likely contain muscle artifacts. |
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Detect segments with NaN and return a new Annotations instance. |
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Get new device to head transform based on good segments. |
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Get the current source density (CSD) transformation. |
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Compute bridged EEG electrodes using the intrinsic Hjorth algorithm. |
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Compute fine calibration from empty-room data. |
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Compute the SSS basis for a given measurement info structure. |
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Compute SSP (signal-space projection) vectors for ECG artifacts. |
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Compute SSP (signal-space projection) vectors for EOG artifacts. |
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Apply cortical signal suppression (CSS) to evoked data. |
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Conveniently generate epochs around ECG artifact events. |
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Conveniently generate epochs around EOG artifact events. |
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Find bad channels using Maxwell filtering. |
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Find ECG events by localizing the R wave peaks. |
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Locate EOG artifacts. |
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Eliminate stimulation's artifacts from instance. |
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Find ECG peaks from one selected ICA source. |
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Locate EOG artifacts from one selected ICA source. |
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Run (extended) Infomax ICA decomposition on raw data. |
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Interpolate bridged electrode pairs. |
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Interpolate or mark bads consistently for a list of instances. |
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Maxwell filter data using multipole moments. |
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Prepare an empty-room recording for Maxwell filtering. |
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Denoise MEG channels using leave-one-out temporal projection. |
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Noise-tolerant fast peak-finding algorithm. |
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Restore ICA solution from fif file. |
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Read an EOG regression model from an HDF5 file. |
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Realign two simultaneous recordings. |
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Remove artifacts using regression based on reference channels. |
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Find similar Independent Components across subjects by map similarity. |
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Load ICA information saved in an EEGLAB .set file. |
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Read fine calibration information from a .dat file. |
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Write fine calibration information to a .dat file. |
NIRS specific preprocessing functions.
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Convert NIRS raw data to optical density. |
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Convert NIRS optical density data to haemoglobin concentration. |
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Determine the distance between NIRS source and detectors. |
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Determine which NIRS channels are short. |
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Calculate scalp coupling index. |
Apply temporal derivative distribution repair to data. |
Intracranial EEG specific preprocessing functions.
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Project sensors onto the brain surface. |
EEG referencing:
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Add reference channels to data that consists of all zeros. |
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Re-reference selected channels using a bipolar referencing scheme. |
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Specify which reference to use for EEG data. |
IIR and FIR filtering and resampling functions.
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Use IIR parameters to get filtering coefficients. |
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Create a FIR or IIR filter. |
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Estimate filter ringing. |
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Filter a subset of channels. |
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Notch filter for the signal x. |
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Resample an array. |
Functions for fitting head positions with (c)HPI coils.
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Compute time-varying cHPI amplitudes. |
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Compute time-varying estimates of cHPI SNR. |
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Compute locations of each cHPI coils over time. |
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Compute time-varying head positions. |
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Extract cHPI locations from CTF data. |
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Extract cHPI locations from KIT data. |
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Remove cHPI and line noise from data. |
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Determine how many HPI coils were active for a time point. |
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Retrieve cHPI information from the data. |
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Convert Maxfilter-formatted head position quaternions. |
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Read MaxFilter-formatted head position parameters. |
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Write MaxFilter-formatted head position parameters. |
Helpers for various transformations.
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A transform. |
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Convert a set of quaternions to rotations. |
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Convert a set of rotations to quaternions. |
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Read a subject's RAS to MNI transform. |