mne_connectivity.spectral_connectivity_epochs

mne_connectivity.spectral_connectivity_epochs(data, names=None, method='coh', indices=None, sfreq=None, mode='multitaper', fmin=None, fmax=inf, fskip=0, faverage=False, tmin=None, tmax=None, mt_bandwidth=None, mt_adaptive=False, mt_low_bias=True, cwt_freqs=None, cwt_n_cycles=7, gc_n_lags=40, rank=None, n_components=1, block_size=1000, n_jobs=1, verbose=None)

Compute frequency- and time-frequency-domain connectivity measures.

The connectivity method(s) are specified using the “method” parameter. All methods are based on estimates of the cross- and power spectral densities (CSD/PSD) Sxy and Sxx, Syy.

Parameters:

dataarray_like, shape=(n_epochs, n_signals, n_times) | Epochs | EpochsSpectrum

The data from which to compute connectivity. Can be epoched timeseries data as an array-like or Epochs object, or Fourier coefficients for each epoch as an EpochsSpectrum object. If timeseries data, the spectral information will be computed according to the spectral estimation mode (see the mode parameter). If an EpochsSpectrum object, this spectral information will be used and the mode parameter will be ignored.

Note that it is also possible to combine multiple timeseries signals by providing a list of tuples, e.g.:

data = [(arr_0, stc_0), (arr_1, stc_1), (arr_2, stc_2)]

which corresponds to 3 epochs where arr_* is an array with the same number of time points as stc_*. Data can also be a list/generator of arrays, shape (n_signals, n_times), or a list/generator of SourceEstimate or VolSourceEstimate objects.

Changed in version 0.8: Fourier coefficients stored in an EpochsSpectrum or EpochsSpectrumArray object can also be passed in as data. Storing Fourier coefficients requires mne >= 1.8.

nameslist | np.ndarray | None

The names of the nodes of the dataset used to compute connectivity. If ‘None’ (default), then names will be a list of integers from 0 to n_nodes. If a list of names, then it must be equal in length to n_nodes.

methodstr | list of str

Connectivity measure(s) to compute. These can be ['coh', 'cohy', 'imcoh', 'cacoh', 'mic', 'mim', 'plv', 'ciplv', 'ppc', 'pli', 'dpli', 'wpli', 'wpli2_debiased', 'gc', 'gc_tr']. These are:

• ‘coh’ : Coherence

• ‘cohy’ : Coherency

• ‘imcoh’ : Imaginary part of Coherency

• ‘cacoh’ : Canonical Coherency (CaCoh)

• ‘mic’ : Maximised Imaginary part of Coherency (MIC)

• ‘mim’ : Multivariate Interaction Measure (MIM)

• ‘plv’ : Phase-Locking Value (PLV)

• ‘ciplv’ : Corrected Imaginary PLV (ciPLV)

• ‘ppc’ : Pairwise Phase Consistency (PPC)

• ‘pli’ : Phase Lag Index (PLI)

• ‘pli2_unbiased’ : Unbiased estimator of squared PLI

• ‘dpli’ : Directed PLI (DPLI)

• ‘wpli’ : Weighted PLI (WPLI)

• ‘wpli2_debiased’ : Debiased estimator of squared WPLI

• ‘gc’ : State-space Granger Causality (GC)

• ‘gc_tr’ : State-space GC on time-reversed signals

Multivariate methods (['cacoh', 'mic', 'mim', 'gc', 'gc_tr']) cannot be called with the other methods.

indicestuple of array | None

Two arrays with indices of connections for which to compute connectivity. If a bivariate method is called, each array for the seeds and targets should contain the channel indices for each bivariate connection. If a multivariate method is called, each array for the seeds and targets should consist of nested arrays containing the channel indices for each multivariate connection. If None, connections between all channels are computed, unless a Granger causality method is called, in which case an error is raised.

sfreqfloat

The sampling frequency. Required if data is an array-like.

modestr

Spectrum estimation mode can be either: ‘multitaper’, ‘fourier’, or ‘cwt_morlet’. Ignored if data is an EpochsSpectrum object.

fminfloat | tuple of float | None

The lower frequency of interest. Multiple bands are defined using a tuple, e.g., (8., 20.) for two bands with 8 Hz and 20 Hz lower freq. If None, the frequency corresponding to 5 cycles based on the epoch length is used. For example, with an epoch length of 1 sec, the lower frequency would be 5 / 1 sec = 5 Hz.

fmaxfloat | tuple of float

The upper frequency of interest. Multiple bands are dedined using a tuple, e.g. (13., 30.) for two band with 13 Hz and 30 Hz upper freq.

fskipint

Omit every “(fskip + 1)-th” frequency bin to decimate in frequency domain.

faveragebool

Average connectivity scores for each frequency band. If True, the output freqs will be a list with arrays of the frequencies that were averaged.

tminfloat | None

Time to start connectivity estimation. Note: when data is an array-like, the first sample is assumed to be at time 0. For Epochs, the time information contained in the object is used to compute the time indices. Ignored if data is an EpochsSpectrum object.

tmaxfloat | None

Time to end connectivity estimation. Note: when data is an array-like, the first sample is assumed to be at time 0. For Epochs, the time information contained in the object is used to compute the time indices. Ignored if data is an EpochsSpectrum object.

mt_bandwidthfloat | None

The bandwidth of the multitaper windowing function in Hz. Only used in ‘multitaper’ mode. Ignored if data is an EpochsSpectrum object.

mt_adaptivebool

Use adaptive weights to combine the tapered spectra into PSD. Only used in ‘multitaper’ mode. Ignored if data is an EpochsSpectrum object.

mt_low_biasbool

Only use tapers with more than 90 percent spectral concentration within bandwidth. Only used in ‘multitaper’ mode. Ignored if data is an EpochsSpectrum object.

cwt_freqsarray

Array of frequencies of interest. Only used in ‘cwt_morlet’ mode. Only the frequencies within the range specified by fmin and fmax are used. Ignored if data is an EpochsSpectrum object.

cwt_n_cyclesfloat | array of float

Number of cycles. Fixed number or one per frequency. Only used in ‘cwt_morlet’ mode. Ignored if data is an EpochsSpectrum object.

gc_n_lagsint

Number of lags to use for the vector autoregressive model when computing Granger causality. Higher values increase computational cost, but reduce the degree of spectral smoothing in the results. Only used if method contains any of ['gc', 'gc_tr'].

ranktuple of array | None

Two arrays with the rank to project the seed and target data to, respectively, using singular value decomposition. If None, the rank of the data is computed and projected to. Only used if method contains any of ['cacoh', 'mic', 'mim', 'gc', 'gc_tr'].

n_componentsint

Number of connectivity components to extract from the data. If an int, the number of components must be <= the minimum rank of the seeds and targets. E.g. if the seed channels had a rank of 5 and the target channels had a rank of 3, n_components must be <= 3. If None, the number of components equal to the minimum rank of the seeds and targets is extracted (see the rank parameter). Only used if method contains any of ['cacoh', 'mic'].

Added in version 0.8.

block_sizeint

How many connections to compute at once (higher numbers are faster but require more memory).

n_jobsint

How many samples to process in parallel.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() for more info). If used, it should be passed as a keyword-argument only.

Returns:

coninstance of SpectralConnectivity or SpectroTemporalConnectivity | list

Computed connectivity measure(s). An instance of SpectralConnectivity, SpectroTemporalConnectivity, or a list of instances corresponding to connectivity measures if several connectivity measures are specified. The shape of the connectivity result will be:

• (n_cons, n_freqs) for multitaper or fourier modes

• (n_cons, n_freqs, n_times) for cwt_morlet mode

• (n_cons, n_comps, n_freqs (, n_times)) for valid multivariate methods if n_components > 1

• n_cons = n_signals ** 2 for bivariate methods with indices=None

• n_cons = 1 for multivariate methods with indices=None

• n_cons = len(indices[0]) for bivariate and multivariate methods when indices is supplied

See also

mne_connectivity.spectral_connectivity_time

mne_connectivity.SpectralConnectivity

mne_connectivity.SpectroTemporalConnectivity

Notes

Please note that the interpretation of the measures in this function depends on the data and underlying assumptions and does not necessarily reflect a causal relationship between brain regions.

These measures are not to be interpreted over time. Each Epoch passed into the dataset is interpreted as an independent sample of the same connectivity structure. Within each Epoch, it is assumed that the spectral measure is stationary. The spectral measures implemented in this function are computed across Epochs. Thus, spectral measures computed with only one Epoch will result in errorful values and spectral measures computed with few Epochs will be unreliable. Please see spectral_connectivity_time() for time-resolved connectivity estimation.

The spectral densities can be estimated using a multitaper method with digital prolate spheroidal sequence (DPSS) windows, a discrete Fourier transform with Hanning windows, or a continuous wavelet transform using Morlet wavelets. The spectral estimation mode is specified using the “mode” parameter.

By default, the connectivity between all signals is computed (only connections corresponding to the lower-triangular part of the connectivity matrix). If one is only interested in the connectivity between some signals, the “indices” parameter can be used. For example, to compute the connectivity between the signal with index 0 and signals “2, 3, 4” (a total of 3 connections) one can use the following:

indices = (np.array([0, 0, 0]),    # row indices
           np.array([2, 3, 4]))    # col indices

con = spectral_connectivity_epochs(data, method='coh',
                                   indices=indices, ...)

In this case con.get_data().shape = (3, n_freqs). The connectivity scores are in the same order as defined indices.

For multivariate methods, this is handled differently. If “indices” is None, connectivity between all signals will be computed and a single connectivity spectrum will be returned (this is not possible if a Granger causality method is called). If “indices” is specified, seed and target indices for each connection should be specified as nested array-likes. For example, to compute the connectivity between signals (0, 1) -> (2, 3) and (0, 1) -> (4, 5), indices should be specified as:

indices = (np.array([[0, 1], [0, 1]]),  # seeds
           np.array([[2, 3], [4, 5]]))  # targets

More information on working with multivariate indices and handling connections where the number of seeds and targets are not equal can be found in the Working with ragged indices for multivariate connectivity example.

Supported Connectivity Measures

The connectivity method(s) is specified using the “method” parameter. The following methods are supported (note: E[] denotes average over epochs). Multiple measures can be computed at once by using a list/tuple, e.g., ['coh', 'pli'] to compute coherence and PLI.

‘coh’ : Coherence given by:

         | E[Sxy] |
C = ---------------------
    sqrt(E[Sxx] * E[Syy])

‘cohy’ : Coherency given by:

           E[Sxy]
C = ---------------------
    sqrt(E[Sxx] * E[Syy])

‘imcoh’ : Imaginary coherence [1] given by:

          Im(E[Sxy])
C = ----------------------
    sqrt(E[Sxx] * E[Syy])

‘cacoh’ : Canonical Coherency (CaCoh) [2] given by:

\(\textrm{CaCoh}=\Large{\frac{\boldsymbol{a}^T\boldsymbol{D} (\Phi)\boldsymbol{b}}{\sqrt{\boldsymbol{a}^T\boldsymbol{a} \boldsymbol{b}^T\boldsymbol{b}}}}\)

where: \(\boldsymbol{D}(\Phi)\) is the cross-spectral density between seeds and targets transformed for a given phase angle \(\Phi\); and \(\boldsymbol{a}\) and \(\boldsymbol{b}\) are eigenvectors for the seeds and targets, such that \(\boldsymbol{a}^T\boldsymbol{D}(\Phi)\boldsymbol{b}\) maximises coherency between the seeds and targets. Taking the absolute value of the results gives maximised coherence.

‘mic’ : Maximised Imaginary part of Coherency (MIC) [3] given by:

\(\textrm{MIC}=\Large{\frac{\boldsymbol{\alpha}^T \boldsymbol{E \beta}}{\parallel\boldsymbol{\alpha}\parallel \parallel\boldsymbol{\beta}\parallel}}\)

where: \(\boldsymbol{E}\) is the imaginary part of the transformed cross-spectral density between seeds and targets; and \(\boldsymbol{\alpha}\) and \(\boldsymbol{\beta}\) are eigenvectors for the seeds and targets, such that \(\boldsymbol{\alpha}^T \boldsymbol{E \beta}\) maximises the imaginary part of coherency between the seeds and targets.

‘mim’ : Multivariate Interaction Measure (MIM) [3] given by:

\(\textrm{MIM}=tr(\boldsymbol{EE}^T)\)

where \(\boldsymbol{E}\) is the imaginary part of the transformed cross-spectral density between seeds and targets.

‘plv’ : Phase-Locking Value (PLV) [4] given by:

PLV = |E[Sxy/|Sxy|]|

‘ciplv’ : corrected imaginary PLV (ciPLV) [5] given by:

                 |E[Im(Sxy/|Sxy|)]|
ciPLV = ------------------------------------
         sqrt(1 - |E[real(Sxy/|Sxy|)]| ** 2)

‘ppc’ : Pairwise Phase Consistency (PPC), an unbiased estimator of squared PLV [6].

‘pli’ : Phase Lag Index (PLI) [7] given by:

PLI = |E[sign(Im(Sxy))]|

‘pli2_unbiased’ : Unbiased estimator of squared PLI [8].

‘dpli’ : Directed Phase Lag Index (DPLI) [9] given by (where H is the Heaviside function):

DPLI = E[H(Im(Sxy))]

‘wpli’ : Weighted Phase Lag Index (WPLI) [8] given by:

          |E[Im(Sxy)]|
WPLI = ------------------
          E[|Im(Sxy)|]

‘wpli2_debiased’ : Debiased estimator of squared WPLI [8].

‘gc’ : State-space Granger Causality (GC) [10] given by:

\(GC = ln\Large{(\frac{\lvert\boldsymbol{S}_{tt}\rvert}{\lvert \boldsymbol{S}_{tt}-\boldsymbol{H}_{ts}\boldsymbol{\Sigma}_{ss \lvert t}\boldsymbol{H}_{ts}^*\rvert}})\)

where: \(s\) and \(t\) represent the seeds and targets, respectively; \(\boldsymbol{H}\) is the spectral transfer function; \(\boldsymbol{\Sigma}\) is the residuals matrix of the autoregressive model; and \(\boldsymbol{S}\) is \(\boldsymbol{\Sigma}\) transformed by \(\boldsymbol{H}\).

‘gc_tr’ : State-space GC on time-reversed signals [10][11] given by the same equation as for ‘gc’, but where the autocovariance sequence from which the autoregressive model is produced is transposed to mimic the reversal of the original signal in time [12].

References

[1]

Guido Nolte, Ou Bai, Lewis Wheaton, Zoltan Mari, Sherry Vorbach, and Mark Hallett. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clinical Neurophysiology, 115(10):2292–2307, 2004. doi:10.1016/j.clinph.2004.04.029.

[2]

Carmen Vidaurre, Guido Nolte, Ingmar E.J. de Vries, M. Gómez, Tjeerd W. Boonstra, K.-R. Müller, Arno Villringer, and Vadim V. Nikulin. Canonical maximization of coherence: a novel tool for investigation of neuronal interactions between two datasets. NeuroImage, 201:116009, 2019. doi:10.1016/j.neuroimage.2019.116009.

[3] (1,2)

Arne Ewald, Laura Marzetti, Filippo Zappasodi, Frank C. Meinecke, and Guido Nolte. Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space. NeuroImage, 60(1):476–488, 2012. doi:10.1016/j.neuroimage.2011.11.084.

[4]

Jean-Philippe Lachaux, Eugenio Rodriguez, Jacques Martinerie, and Francisco J. Varela. Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4):194–208, 1999. doi:10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C.

[5]

Ernesto Pereda Ricardo Bruña, Fernando Maestú. Phase locking value revisited: teaching new tricks to an old dog. Journal of Neural Engineering, 15(5):056011, 2018. doi:10.1088/1741-2552/aacfe4.

[6]

Martin Vinck, Marijn van Wingerden, Thilo Womelsdorf, Pascal Fries, and Cyriel M.A. Pennartz. The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization. NeuroImage, 51(1):112–122, 2010. doi:10.1016/j.neuroimage.2010.01.073.

[7]

Cornelis J. Stam, Guido Nolte, and Andreas Daffertshofer. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human Brain Mapping, 28(11):1178–1193, 2007. doi:10.1002/hbm.20346.

[8] (1,2,3)

Martin Vinck, Robert Oostenveld, Marijn van Wingerden, Franscesco Battaglia, and Cyriel M.A. Pennartz. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. NeuroImage, 55(4):1548–1565, 2011. doi:10.1016/j.neuroimage.2011.01.055.

[9]

C. J. Stam and E. C. W. van Straaten. Go with the flow: use of a directed phase lag index (dpli) to characterize patterns of phase relations in a large-scale model of brain dynamics. NeuroImage, 62(3):1415–1428, Sep 2012. doi:10.1016/j.neuroimage.2012.05.050.

[10] (1,2)

Lionel Barnett and Anil K. Seth. Granger causality for state-space models. Physical Review E, 91(4):040101, 2015. doi:10.1103/PhysRevE.91.040101.

[11]

Irene Winkler, Danny Panknin, Daniel Bartz, Klaus-Robert Müller, and Stefan Haufe. Validity of time reversal for testing granger causality. IEEE Transactions on Signal Processing, 64(11):2746–2760, 2016. doi:10.1109/TSP.2016.2531628.

[12]

Stefan Haufe, Vadim V Nikulin, and Guido Nolte. Alleviating the influence of weak data asymmetries on granger-causal analyses. In Latent Variable Analysis and Signal Separation: 10th International Conference, LVA/ICA 2012, Tel Aviv, Israel, March 12-15, 2012. Proceedings 10, 25–33. Springer, 2012. doi:10.1007/978-3-642-28551-6_4.

Examples using mne_connectivity.spectral_connectivity_epochs

Comparing PLI, wPLI, and dPLI

Comparing PLI, wPLI, and dPLI

Comparing spectral connectivity computed over time or over trials

Comparing spectral connectivity computed over time or over trials

Comparison of coherency-based methods

Comparison of coherency-based methods

Compute all-to-all connectivity in sensor space

Compute all-to-all connectivity in sensor space

Compute coherence in source space using a MNE inverse solution

Compute coherence in source space using a MNE inverse solution

Compute directionality of connectivity with multivariate Granger causality

Compute directionality of connectivity with multivariate Granger causality

Compute full spectrum source space connectivity between labels

Compute full spectrum source space connectivity between labels

Compute mixed source space connectivity and visualize it using a circular graph

Compute mixed source space connectivity and visualize it using a circular graph

Compute multivariate coherency/coherence

Compute multivariate coherency/coherence

Compute multivariate measures of the imaginary part of coherency

Compute multivariate measures of the imaginary part of coherency

Compute seed-based time-frequency connectivity in sensor space

Compute seed-based time-frequency connectivity in sensor space

Compute source space connectivity and visualize it using a circular graph

Compute source space connectivity and visualize it using a circular graph

Using the connectivity classes

Using the connectivity classes

Working with ragged indices for multivariate connectivity

Working with ragged indices for multivariate connectivity

Multivariate decomposition for efficient connectivity analysis

Multivariate decomposition for efficient connectivity analysis