| Parameters: | raw : instance 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 of int, 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
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.
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.
covariates : dict-like | None
If dict-like (e.g., a pandas DataFrame), values have to be array-like
and of the same length as the columns 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 peak-to-peak 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 peak-to-peak values to select rejected
epochs, e.g.:
reject = dict(grad=4000e-12, # T / m (gradiometers)
mag=4e-11, # T (magnetometers)
eeg=40e-5, # V (EEG channels)
eog=250e-5 # V (EOG channels))
flat : None | dict
or 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 peak-to-peak values to
select rejected epochs.
tstep : float
Length of windows for peak-to-peak 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 : None | list
List of indices of channels to be included. If None, defaults to all
MEG and EEG channels.
solver : str | function
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. If str, must be 'cholesky', in which case
the solver used is linalg.solve(dot(X.T, X), dot(X.T, y)).
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