mne.minimum_norm.compute_source_psd#
- mne.minimum_norm.compute_source_psd(raw, inverse_operator, lambda2=0.1111111111111111, method='dSPM', tmin=0.0, tmax=None, fmin=0.0, fmax=200.0, n_fft=2048, overlap=0.5, pick_ori=None, label=None, nave=1, pca=True, prepared=False, method_params=None, inv_split=None, bandwidth='hann', adaptive=False, low_bias=False, n_jobs=None, return_sensor=False, dB=False, *, verbose=None)[source]#
Compute source power spectral density (PSD).
- Parameters:
- rawinstance of
Raw
The raw data.
- inverse_operatorinstance of
InverseOperator
The inverse operator.
- lambda2
float
The regularization parameter.
- method“MNE” | “dSPM” | “sLORETA”
Use minimum norm, dSPM (default), sLORETA, or eLORETA.
- tmin
float
The beginning of the time interval of interest (in seconds). Use 0. for the beginning of the file.
- tmax
float
|None
The end of the time interval of interest (in seconds). If None stop at the end of the file.
- fmin
float
The lower frequency of interest.
- fmax
float
The upper frequency of interest.
- n_fft
int
Window size for the FFT. Should be a power of 2.
- overlap
float
The overlap fraction between windows. Should be between 0 and 1. 0 means no overlap.
- pick_ori
None
| “normal” If “normal”, rather than pooling the orientations by taking the norm, only the radial component is kept. This is only implemented when working with loose orientations.
- label
Label
Restricts the source estimates to a given label.
- nave
int
The number of averages used to scale the noise covariance matrix.
- 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).
- prepared
bool
If True, do not call
prepare_inverse_operator()
.- method_params
dict
|None
Additional options for eLORETA. See Notes of
apply_inverse()
.New in version 0.16.
- inv_split
int
orNone
Split inverse operator into inv_split parts in order to save memory.
New in version 0.17.
- bandwidth
float
|str
The bandwidth of the multi taper windowing function in Hz. Can also be a string (e.g., ‘hann’) to use a single window.
For backward compatibility, the default is ‘hann’.
New in version 0.17.
- adaptive
bool
Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).
New in version 0.17.
- low_bias
bool
Only use tapers with more than 90% spectral concentration within bandwidth.
New in version 0.17.
- n_jobs
int
|None
The number of jobs to run in parallel. If
-1
, it is set to the number of CPU cores. Requires thejoblib
package.None
(default) is a marker for ‘unset’ that will be interpreted asn_jobs=1
(sequential execution) unless the call is performed under ajoblib.parallel_backend()
context manager that sets another value forn_jobs
. It is only used if adaptive=True.New in version 0.17.
- return_sensor
bool
If True, return the sensor PSDs as an EvokedArray.
New in version 0.17.
- dB
bool
If True (default False), return output it decibels.
New in version 0.17.
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- rawinstance of
- Returns:
- stc_psdinstance of
SourceEstimate
|VolSourceEstimate
The PSD of each of the sources.
- sensor_psdinstance of
EvokedArray
The PSD of each sensor. Only returned if
return_sensor
is True.
- stc_psdinstance of
See also
Notes
Each window is multiplied by a window before processing, so using a non-zero overlap is recommended.
This function is different from
compute_source_psd_epochs()
in that:bandwidth='hann'
by default, skipping multitaper estimationFor convenience it wraps
mne.make_fixed_length_events()
andmne.Epochs
.
Otherwise the two should produce identical results.
Examples using mne.minimum_norm.compute_source_psd
#
Compute source power spectral density (PSD) in a label
Compute source power spectral density (PSD) of VectorView and OPM data