mne.preprocessing.EOGRegression#
- class mne.preprocessing.EOGRegression(picks=None, exclude='bads', picks_artifact='eog', proj=True)[source]#
Remove EOG artifact signals from other channels by regression.
Employs linear regression to remove signals captured by some channels, typically EOG, as described in [1]. You can also choose to fit the regression coefficients on evoked blink/saccade data and then apply them to continuous data, as described in [2].
- Parameters:
- picks
str
| array_like |slice
|None
Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g.,
['meg', 'eeg']
) will pick channels of those types, channel name strings (e.g.,['MEG0111', 'MEG2623']
will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels. Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided.- exclude
list
| ‘bads’ List of channels to exclude from the regression, only used when picking based on types (e.g., exclude=”bads” when picks=”meg”). Specify
'bads'
(the default) to exclude all channels marked as bad.- picks_artifactarray_like |
str
Channel picks to use as predictor/explanatory variables capturing the artifact of interest (default is “eog”).
- proj
bool
Whether to automatically apply SSP projection vectors before fitting and applying the regression. Default is
True
.
- picks
Notes
New in version 1.2.
References
- Attributes:
- coef_
ndarray
, shape (n, n) The regression coefficients. Only available after fitting.
- info_
Info
Channel information corresponding to the regression weights. Only available after fitting.
- picksarray_like |
str
Channels to perform the regression on.
- exclude
list
| ‘bads’ Channels to exclude from the regression.
- picks_artifactarray_like |
str
The channels designated as containing the artifacts of interest.
- proj
bool
Whether projections will be applied before performing the regression.
- coef_
Methods
apply
(inst[, copy])Apply the regression coefficients to data.
fit
(inst)Fit EOG regression coefficients.
plot
([ch_type, sensors, show_names, mask, ...])Plot the regression weights of a fitted EOGRegression model.
save
(fname[, overwrite])Save the regression model to an HDF5 file.
- apply(inst, copy=True)[source]#
Apply the regression coefficients to data.
- Parameters:
- Returns:
Notes
Only works after
.fit()
has been used.References
Examples using
apply
:Repairing artifacts with regression
Repairing artifacts with regressionReduce EOG artifacts through regression
Reduce EOG artifacts through regression
- fit(inst)[source]#
Fit EOG regression coefficients.
- Parameters:
- Returns:
- self
EOGRegression
The fitted
EOGRegression
object. The regression coefficients are available as the.coef_
and.intercept_
attributes.
- self
Notes
If your data contains EEG channels, make sure to apply the desired reference (see
mne.set_eeg_reference()
) before performing EOG regression.
- plot(ch_type=None, sensors=True, show_names=False, mask=None, mask_params=None, contours=6, outlines='head', sphere=None, image_interp='cubic', extrapolate='auto', border='mean', res=64, size=1, cmap=None, vlim=(None, None), cnorm=None, axes=None, colorbar=True, cbar_fmt='%1.1e', title=None, show=True)[source]#
Plot the regression weights of a fitted EOGRegression model.
- Parameters:
- ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ |
None
The channel type to plot. For
'grad'
, the gradiometers are collected in pairs and the RMS for each pair is plotted. IfNone
the first available channel type from order shown above is used. Defaults toNone
.- sensors
bool
|str
Whether to add markers for sensor locations. If
str
, should be a valid matplotlib format string (e.g.,'r+'
for red plusses, see the Notes section ofplot()
). IfTrue
(the default), black circles will be used.- show_names
bool
|callable()
If
True
, show channel names next to each sensor marker. If callable, channel names will be formatted using the callable; e.g., to delete the prefix ‘MEG ‘ from all channel names, pass the functionlambda x: x.replace('MEG ', '')
. Ifmask
is notNone
, only non-masked sensor names will be shown.- mask
ndarray
ofbool
, shape (n_channels,) |None
Array indicating channel(s) to highlight with a distinct plotting style. Array elements set to
True
will be plotted with the parameters given inmask_params
. Defaults toNone
, equivalent to an array of allFalse
elements.- mask_params
dict
|None
Additional plotting parameters for plotting significant sensors. Default (None) equals:
dict(marker='o', markerfacecolor='w', markeredgecolor='k', linewidth=0, markersize=4)
- contours
int
| array_like The number of contour lines to draw. If
0
, no contours will be drawn. If a positive integer, that number of contour levels are chosen using the matplotlib tick locator (may sometimes be inaccurate, use array for accuracy). If array-like, the array values are used as the contour levels. The values should be in µV for EEG, fT for magnetometers and fT/m for gradiometers. Ifcolorbar=True
, the colorbar will have ticks corresponding to the contour levels. Default is6
.- outlines‘head’ |
dict
|None
The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.
- sphere
float
| array_like | instance ofConductorModel
|None
| ‘auto’ | ‘eeglab’ The sphere parameters to use for the head outline. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give just the radius (origin assumed 0, 0, 0). Can also be an instance of a spherical
ConductorModel
to use the origin and radius from that object. If'auto'
the sphere is fit to digitization points. If'eeglab'
the head circle is defined by EEG electrodes'Fpz'
,'Oz'
,'T7'
, and'T8'
(if'Fpz'
is not present, it will be approximated from the coordinates of'Oz'
).None
(the default) is equivalent to'auto'
when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.New in version 0.20.
Changed in version 1.1: Added
'eeglab'
option.- image_interp
str
The image interpolation to be used. Options are
'cubic'
(default) to usescipy.interpolate.CloughTocher2DInterpolator
,'nearest'
to usescipy.spatial.Voronoi
or'linear'
to usescipy.interpolate.LinearNDInterpolator
.- extrapolate
str
Options:
'box'
Extrapolate to four points placed to form a square encompassing all data points, where each side of the square is three times the range of the data in the respective dimension.
'local'
(default for MEG sensors)Extrapolate only to nearby points (approximately to points closer than median inter-electrode distance). This will also set the mask to be polygonal based on the convex hull of the sensors.
'head'
(default for non-MEG sensors)Extrapolate out to the edges of the clipping circle. This will be on the head circle when the sensors are contained within the head circle, but it can extend beyond the head when sensors are plotted outside the head circle.
Changed in version 0.21:
The default was changed to
'local'
for MEG sensors.'local'
was changed to use a convex hull mask'head'
was changed to extrapolate out to the clipping circle.
- border
float
| ‘mean’ Value to extrapolate to on the topomap borders. If
'mean'
(default), then each extrapolated point has the average value of its neighbours.New in version 0.20.
- res
int
The resolution of the topomap image (number of pixels along each side).
- size
float
Side length of each subplot in inches.
- cmapmatplotlib colormap | (colormap,
bool
) | ‘interactive’ |None
Colormap to use. If
tuple
, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. IfNone
,'Reds'
is used for data that is either all-positive or all-negative, and'RdBu_r'
is used otherwise.'interactive'
is equivalent to(None, True)
. Defaults toNone
.Warning
Interactive mode works smoothly only for a small amount of topomaps. Interactive mode is disabled by default for more than 2 topomaps.
- vlim
tuple
of length 2 Colormap limits to use. If a
tuple
of floats, specifies the lower and upper bounds of the colormap (in that order); providingNone
for either entry will set the corresponding boundary at the min/max of the data. Defaults to(None, None)
.- cnorm
matplotlib.colors.Normalize
|None
How to normalize the colormap. If
None
, standard linear normalization is performed. If notNone
,vmin
andvmax
will be ignored. See Matplotlib docs for more details on colormap normalization, and the ERDs example for an example of its use.- axesinstance of
Axes
|list
ofAxes
|None
The axes to plot to. If
None
, a newFigure
will be created with the correct number of axes. IfAxes
are provided (either as a single instance or alist
of axes), the number of axes provided must match the number oftimes
provided (unlesstimes
isNone
).Default isNone
.- colorbar
bool
Plot a colorbar in the rightmost column of the figure.
- cbar_fmt
str
Formatting string for colorbar tick labels. See Format Specification Mini-Language for details.
- title
str
|None
The title of the generated figure. If
None
(default), no title is displayed.- show
bool
Show the figure if
True
.
- ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ |
- Returns:
- figinstance of
matplotlib.figure.Figure
Figure with a topomap subplot for each channel type.
- figinstance of
Notes
New in version 1.2.
Examples using
plot
:Repairing artifacts with regression
Repairing artifacts with regressionReduce EOG artifacts through regression
Reduce EOG artifacts through regression
Examples using mne.preprocessing.EOGRegression
#
Repairing artifacts with regression
Reduce EOG artifacts through regression