# 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 : floatSampling rate frequencies : array-likeThe frequencies use_fft : boolUse the FFT for convolutions or not. n_cycles : float | array of floatNumber of cycles in the Morlet wavelet. Fixed number or one per frequency. baseline : None (default) or tuple of length 2The 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 : arrayRequired to define baseline decim : int | sliceTo 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 : intThe number of epochs to process at the same time zero_mean : boolMake sure the wavelets have a mean of zero. verbose : bool, str, int, or NoneIf not None, override default verbose level (see mne.verbose). power : 4D array Power estimate (Epochs x Channels x Frequencies x Timepoints).