# mne.time_frequency.single_trial_power¶

mne.time_frequency.single_trial_power(data, sfreq, frequencies, use_fft=True, n_cycles=7, baseline=None, baseline_mode='ratio', times=None, decim=1, n_jobs=1, zero_mean=False, verbose=None)

Compute time-frequency power on single epochs

Parameters: data : array of 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’ 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)) times : array Required to define baseline decim : int Temporal decimation factor n_jobs : int The number of epochs to process at the same time zero_mean : bool Make sure the wavelets are zero mean. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose). power : 4D array Power estimate (Epochs x Channels x Frequencies x Timepoints).