mne.minimum_norm.source_band_induced_power¶

mne.minimum_norm.source_band_induced_power(epochs, inverse_operator, bands, label=None, lambda2=0.1111111111111111, method=’dSPM’, nave=1, n_cycles=5, df=1, use_fft=False, decim=1, baseline=None, baseline_mode=’logratio’, pca=True, n_jobs=1, prepared=False, verbose=None)[source]¶

Compute source space induced power in given frequency bands.

Parameters:

epochs : instance of Epochs The epochs. inverse_operator : instance of inverse operator The inverse operator. bands : dict Example : bands = dict(alpha=[8, 9]). label : Label Restricts the source estimates to a given label. lambda2 : float The regularization parameter of the minimum norm. method : “MNE” | “dSPM” | “sLORETA” Use mininum norm, dSPM or sLORETA. nave : int The number of averages used to scale the noise covariance matrix. n_cycles : float | array of float Number of cycles. Fixed number or one per frequency. df : float delta frequency within bands. use_fft : bool Do convolutions in time or frequency domain with FFT. decim : int Temporal decimation factor. 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 to (None, None) all the time interval is used. baseline_mode : None | ‘logratio’ | ‘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)). pca : bool If True, the true dimension of data is estimated before running the time-frequency transforms. It reduces the computation times e.g. with a dataset that was maxfiltered (true dim is 64). n_jobs : int Number of jobs to run in parallel. prepared : bool If True, do not call prepare_inverse_operator. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

stcs : dict with a SourceEstimate (or VolSourceEstimate) for each band The estimated source space induced power estimates.

Examples using mne.minimum_norm.source_band_induced_power¶

Compute induced power in the source space with dSPM