Estimates frequency-resolved directed connectivity with Partial Directed
Coherence (Baccala & Sameshima 2001), computed from the frequency-domain
coefficient matrix \(\bar{A}(f)\) of an MVAR model
(PhysioExperiment::mvarFit()). Unlike DTF, PDC reflects only direct
channel-to-channel influences, so a purely indirect pathway gives PDC near
zero. The (default) generalized PDC weights each row by the inverse residual
standard deviation to make the measure scale-invariant (Baccala 2007). PDC
satisfies \(\sum_i \mathrm{PDC}_{ij}(f)^2 = 1\) for each source \(j\)
(outflow normalization).
Usage
eegPDC(
x,
order = NULL,
freqs = NULL,
generalized = TRUE,
band = NULL,
method = "ols",
assay_name = NULL
)Arguments
- x
A PhysioExperiment object with 2D EEG data (time x channels).
- order
MVAR model order, or
NULLto select automatically.- freqs
Numeric vector of frequencies in Hz (default: 128 points from 0 to the Nyquist frequency).
- generalized
Use generalized PDC (default:
TRUE).- band
Optional numeric length-2 band in Hz over which to average the stored connectivity matrix (default: all frequencies).
- method
MVAR estimator passed to
PhysioExperiment::mvarFit()(default:"ols").- assay_name
Input assay name (default: the default assay).
Value
The PhysioExperiment with metadata(x)$connectivity set as in
eegDTF() (a band-averaged directed matrix, the frequency-resolved
array, frequencies, and settings).
References
Baccala, L. A., & Sameshima, K. (2001). Partial directed coherence: a new concept in neural structure determination. Biological Cybernetics, 84(6), 463-474.
Examples
pe <- make_eeg(n_time = 4000, n_channels = 5, sr = 250)
pe <- eegPDC(pe, order = 5)
S4Vectors::metadata(pe)$connectivity$matrix
#> Fp1 Fp2 F7 F3 Fz
#> Fp1 0.9308886 0.20475110 0.09796020 0.10207939 0.1918640
#> Fp2 0.1188708 0.95332157 0.17014541 0.08760945 0.1391627
#> F7 0.1051502 0.12406314 0.96623214 0.07591603 0.1297131
#> F3 0.1390672 0.04612233 0.04757274 0.96540989 0.1932865
#> Fz 0.2153827 0.09414714 0.06520345 0.16158563 0.9137596