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Time-frequency analysis

time_frequency_conditions module-attribute

Python
time_frequency_conditions: Sequence[str] = []

The conditions to compute time-frequency decomposition on.

Example
Python
time_frequency_conditions = ['left', 'right']
Pipeline steps using this setting

The following steps are directly affected by changes to time_frequency_conditions:

  • sensor/_04_time_frequency

time_frequency_freq_min module-attribute

Python
time_frequency_freq_min: float | None = 8

Minimum frequency for the time frequency analysis, in Hz.

Example
Python
time_frequency_freq_min = 0.3  # 0.3 Hz
Pipeline steps using this setting

The following steps are directly affected by changes to time_frequency_freq_min:

  • sensor/_04_time_frequency
  • sensor/_05_decoding_csp
  • sensor/_99_group_average

time_frequency_freq_max module-attribute

Python
time_frequency_freq_max: float | None = 40

Maximum frequency for the time frequency analysis, in Hz.

Example
Python
time_frequency_freq_max = 22.3  # 22.3 Hz
Pipeline steps using this setting

The following steps are directly affected by changes to time_frequency_freq_max:

  • sensor/_04_time_frequency
  • sensor/_05_decoding_csp
  • sensor/_99_group_average

time_frequency_cycles module-attribute

Python
time_frequency_cycles: float | FloatArrayLike | None = 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.

Pipeline steps using this setting

The following steps are directly affected by changes to time_frequency_cycles:

  • sensor/_04_time_frequency

time_frequency_subtract_evoked module-attribute

Python
time_frequency_subtract_evoked: bool = 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.

Pipeline steps using this setting

The following steps are directly affected by changes to time_frequency_subtract_evoked:

  • sensor/_04_time_frequency
  • sensor/_05_decoding_csp

time_frequency_baseline module-attribute

Python
time_frequency_baseline: tuple[float, float] | None = None

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

Example
Python
time_frequency_baseline = (None, 0)
Pipeline steps using this setting

The following steps are directly affected by changes to time_frequency_baseline:

  • sensor/_04_time_frequency

time_frequency_baseline_mode module-attribute

Python
time_frequency_baseline_mode: str = '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'
Pipeline steps using this setting

The following steps are directly affected by changes to time_frequency_baseline_mode:

  • sensor/_04_time_frequency

time_frequency_crop module-attribute

Python
time_frequency_crop: dict[str, float] | None = 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.)
Pipeline steps using this setting

The following steps are directly affected by changes to time_frequency_crop:

  • sensor/_04_time_frequency