| 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 | 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.
- 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 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 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.
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 is
linalg.solve(dot(X.T, X), dot(X.T, y)).
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