- mne.baseline.rescale(data, times, baseline, mode='mean', copy=True, picks=None, verbose=None)[source]#
Rescale (baseline correct) data.
It can be of any shape. The only constraint is that the last dimension should be time.
Time instants is seconds.
tupleof length 2
The time interval to consider as “baseline” when applying baseline correction. If
None, do not apply baseline correction. If a tuple
(a, b), the interval is between
b(in seconds), including the endpoints. If
None, the beginning of the data is used; and if
None, it is set to the end of the interval. If
(None, None), the entire time interval is used.
(a, b)includes both endpoints, i.e. all timepoints
a <= t <= b.
- mode‘mean’ | ‘ratio’ | ‘logratio’ | ‘percent’ | ‘zscore’ | ‘zlogratio’
Perform baseline correction by
subtracting the mean of baseline values (‘mean’)
dividing by the mean of baseline values (‘ratio’)
dividing by the mean of baseline values and taking the log (‘logratio’)
subtracting the mean of baseline values followed by dividing by the mean of baseline values (‘percent’)
subtracting the mean of baseline values and dividing by the standard deviation of baseline values (‘zscore’)
dividing by the mean of baseline values, taking the log, and dividing by the standard deviation of log baseline values (‘zlogratio’)
Whether to return a new instance or modify in place.
Data to process along the axis=-2 (None, default, processes all).
Control verbosity of the logging output. If
None, use the default verbosity level. See the logging documentation and
mne.verbose()for details. Should only be passed as a keyword argument.
Array of same shape as data after rescaling.
Explore event-related dynamics for specific frequency bands
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)