mne.stats.linear_regression_raw¶

mne.stats.
linear_regression_raw
(raw, events, event_id=None, tmin=0.1, tmax=1, covariates=None, reject=None, flat=None, tstep=1.0, decim=1, picks=None, solver='cholesky')[source]¶ Estimate regressionbased evoked potentials/fields by linear modeling.
This models the full M/EEG time course, including correction for overlapping potentials and allowing for continuous/scalar predictors. Internally, this constructs a predictor matrix X of size n_samples * (n_conds * window length), solving the linear system
Y = bX
and returningb
as evokedlike time series split by condition. See [1]. Parameters
 rawinstance of
Raw
A raw object. Note: be very careful about data that is not downsampled, as the resulting matrices can be enormous and easily overload your computer. Typically, 100 Hz sampling rate is appropriate  or using the decim keyword (see below).
 events
ndarray
ofint
, shape (n_events, 3) An array where the first column corresponds to samples in raw and the last to integer codes in event_id.
 event_id
dict
None
As in Epochs; a dictionary where the values may be integers or iterables of integers, corresponding to the 3rd column of events, and the keys are condition names. If None, uses all events in the events array.
 tmin
float
dict
If float, gives the lower limit (in seconds) for the time window for which all event types’ effects are estimated. If a dict, can be used to specify time windows for specific event types: keys correspond to keys in event_id and/or covariates; for missing values, the default (.1) is used.
 tmax
float
dict
If float, gives the upper limit (in seconds) for the time window for which all event types’ effects are estimated. If a dict, can be used to specify time windows for specific event types: keys correspond to keys in event_id and/or covariates; for missing values, the default (1.) is used.
 covariatesdictlike 
None
If dictlike (e.g., a pandas DataFrame), values have to be arraylike and of the same length as the rows in
`events`
. Keys correspond to additional event types/conditions to be estimated and are matched with the time points given by the first column of`events`
. If None, only binary events (from event_id) are used. reject
None
dict
For cleaning raw data before the regression is performed: set up rejection parameters based on peaktopeak amplitude in continuously selected subepochs. If None, no rejection is done. If dict, keys are types (‘grad’  ‘mag’  ‘eeg’  ‘eog’  ‘ecg’) and values are the maximal peaktopeak values to select rejected epochs, e.g.:
reject = dict(grad=4000e12, # T / m (gradiometers) mag=4e11, # T (magnetometers) eeg=40e5, # V (EEG channels) eog=250e5 # V (EOG channels))
 flat
None
dict
For cleaning raw data before the regression is performed: set up rejection parameters based on flatness of the signal. If None, no rejection is done. If a dict, keys are (‘grad’  ‘mag’  ‘eeg’  ‘eog’  ‘ecg’) and values are minimal peaktopeak values to select rejected epochs.
 tstep
float
Length of windows for peaktopeak detection for raw data cleaning.
 decim
int
Decimate by choosing only a subsample of data points. Highly recommended for data recorded at high sampling frequencies, as otherwise huge intermediate matrices have to be created and inverted.
 picks
str
list
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. solver
str
callable()
Either a function which takes as its inputs the sparse predictor matrix X and the observation matrix Y, and returns the coefficient matrix b; or a string. X is of shape (n_times, n_predictors * time_window_length). y is of shape (n_channels, n_times). If str, must be
'cholesky'
, in which case the solver used islinalg.solve(dot(X.T, X), dot(X.T, y))
.
 rawinstance of
 Returns
 evokeds
dict
A dict where the keys correspond to conditions and the values are Evoked objects with the ER[F/P]s. These can be used exactly like any other Evoked object, including e.g. plotting or statistics.
 evokeds
References
 1
Smith, N. J., & Kutas, M. (2015). Regressionbased estimation of ERP waveforms: II. Nonlinear effects, overlap correction, and practical considerations. Psychophysiology, 52(2), 169189.