k,l,,m,n	Indices for vector/matrix elements, time points, frequency components.
t	Time.
ν	Frequency
ω	pulsation = 2 * π*ν
x,y	Generic vector processes.
e	Generic noise process.
ρ	Generic complex signal amplitude
φ	Generic complex signal phase
f	Generic scalar function.
V	Observations (M/EEG sensor signal) vector
ι	States (source activity) vector
ξ	Sensor signal noise vector
ζ	Biological noise vector
u	Exogenous inputs (stimulus) vector
L	Source to observations (M/EEG sensor signal) transfer function (Lead Field) matrix
s	Empirical covariance (cross-spectral) matrix.
Σ	Population covariance (cross-spectral) matrix.
K	Neural connectivity matrix or source connectivity.
T	Neural lag matrix
A	(uppercase alpha) Multivariate Autoregressive (MVAR) model coefficients matrix
B	(uppercase Beta) Multivariate Autoregressive (MVAR) model Transfer Function (TF).
GCx→y	Granger Causality from y to x
${\mathrm{B}}_{m\to n}^2$	Multivariate Autoregressive (MVAR) model Directed Transfer Function (DTF) from m to n
${\Gamma }_{m\to n}^2$	Normalized DTF
Rm↔n	Pearson Correlation matrix coefficient of the m-th and n-th vector components
Γ	Normalized DTF
Π	Partial Directed Coherence (PDC) matrix
${\Pi }_{m\to n}^2(v)$	Squared amplitude of the PCD component m,n (magnitude of the influence from m to n)
${}_{\mathcal{C}}\Pi$, ${}_{\mathcal{R}}\Pi$, ${}_{\mathcal{G}}\Pi$, ${}_{\mathcal{J}}\Pi$, ${}_{\mathcal{W}}\Pi$	PDC normalization versions: column-wise, row-wise, generalized, information, weighted
δ	Gradient of the PDC squared absolute values
γ2	PCD’s normal tendency squared variance
H	Hilbert Transform
PLV	Phase locking value (PLV)
${}_{\mathcal{W}}\text{PLV}$, ${}_{\mathcal{J}}\text{PLV}$	weighted PLV, information PLV