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Time-frequency analysis

time_frequency_conditions module-attribute

Python
time_frequency_conditions = []

The conditions to compute time-frequency decomposition on.

Example
Python
time_frequency_conditions = ['left', 'right']

time_frequency_freq_min module-attribute

Python
time_frequency_freq_min = 8

Minimum frequency for the time frequency analysis, in Hz.

Example
Python
time_frequency_freq_min = 0.3  # 0.3 Hz

time_frequency_freq_max module-attribute

Python
time_frequency_freq_max = 40

Maximum frequency for the time frequency analysis, in Hz.

Example
Python
time_frequency_freq_max = 22.3  # 22.3 Hz

time_frequency_cycles module-attribute

Python
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

Python
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

Python
time_frequency_baseline = None

Baseline period to use for the time-frequency analysis. If None, no baseline.

Example
Python
time_frequency_baseline = (None, 0)

time_frequency_baseline_mode module-attribute

Python
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
Python
time_frequency_baseline_mode = 'mean'

time_frequency_crop module-attribute

Python
time_frequency_crop = None

Period and frequency range to crop the time-frequency analysis to. If None, no cropping.

Example
Python
time_frequency_crop = dict(tmin=-0.3, tmax=0.5, fmin=5, fmax=20)