Time-frequency analysis
time_frequency_conditions
module-attribute
¶
time_frequency_conditions = []
The conditions to compute time-frequency decomposition on.
Example
time_frequency_conditions = ['left', 'right']
time_frequency_freq_min
module-attribute
¶
time_frequency_freq_min = 8
Minimum frequency for the time frequency analysis, in Hz.
Example
time_frequency_freq_min = 0.3 # 0.3 Hz
time_frequency_freq_max
module-attribute
¶
time_frequency_freq_max = 40
Maximum frequency for the time frequency analysis, in Hz.
Example
time_frequency_freq_max = 22.3 # 22.3 Hz
time_frequency_cycles
module-attribute
¶
time_frequency_cycles = None
The number of cycles to use in the Morlet wavelet. This can be a single number
or one per frequency, where frequencies are calculated via
np.arange(time_frequency_freq_min, time_frequency_freq_max)
.
If None
, uses
np.arange(time_frequency_freq_min, time_frequency_freq_max) / 3
.
time_frequency_subtract_evoked
module-attribute
¶
time_frequency_subtract_evoked = False
Whether to subtract the evoked response (averaged across all epochs) from the
epochs before passing them to time-frequency analysis. Set this to True
to
highlight induced activity.
Info
This also applies to CSP analysis.
time_frequency_baseline
module-attribute
¶
time_frequency_baseline = None
Baseline period to use for the time-frequency analysis. If None
, no baseline.
Example
time_frequency_baseline = (None, 0)
time_frequency_baseline_mode
module-attribute
¶
time_frequency_baseline_mode = 'mean'
Baseline mode to use for the time-frequency analysis. Can be chosen among: "mean" or "ratio" or "logratio" or "percent" or "zscore" or "zlogratio".
Example
time_frequency_baseline_mode = 'mean'
time_frequency_crop
module-attribute
¶
time_frequency_crop = None
Period and frequency range to crop the time-frequency analysis to.
If None
, no cropping.
Example
time_frequency_crop = dict(tmin=-0.3, tmax=0.5, fmin=5, fmax=20)