mne.time_frequency.single_trial_power

mne.time_frequency.single_trial_power(*args, **kwargs)

Warning

DEPRECATED: This function will be removed in mne 0.14; use mne.time_frequency.tfr_morlet() with average=False instead.

Compute time-frequency power on single epochs

Parameters:

data : array, shape (n_epochs, n_channels, n_times)

The epochs

sfreq
: float

Sampling rate

frequencies
: array-like

The frequencies

use_fft
: bool

Use the FFT for convolutions or not.

n_cycles
: float | array of float

Number of cycles in the Morlet wavelet. Fixed number or one per frequency.

baseline
: None (default) or tuple of length 2

The time interval to apply baseline correction. If None do not apply it. If baseline is (a, b) the interval is between “a (s)” and “b (s)”. If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal ot (None, None) all the time interval is used.

baseline_mode
: None | ‘ratio’ | ‘zscore’ | ‘mean’ | ‘percent’ | ‘logratio’ | ‘zlogratio’

Do baseline correction with ratio (power is divided by mean power during baseline) or zscore (power is divided by standard deviation of power during baseline after subtracting the mean, power = [power - mean(power_baseline)] / std(power_baseline)), mean simply subtracts the mean power, percent is the same as applying ratio then mean, logratio is the same as mean but then rendered in log-scale, zlogratio is the same as zscore but data is rendered in log-scale first. If None no baseline correction is applied.

times
: array

Required to define baseline

decim
: int | slice

To reduce memory usage, decimation factor after time-frequency decomposition. If int, returns tfr[..., ::decim]. If slice, returns tfr[..., decim].

Note

Decimation may create aliasing artifacts.

Defaults to 1.

n_jobs
: int

The number of epochs to process at the same time

zero_mean
: bool

Make sure the wavelets have a mean of zero.

verbose
: bool, str, int, or None

If not None, override default verbose level (see mne.verbose).
Returns:

power : 4D array

Power estimate (Epochs x Channels x Frequencies x Timepoints).