Principal components analysis (PCA): For the analysis, we introduce the following notation.
Let Jt , i; , s  be the current density in voxel i, as estimated by LORETA, in condition  at t
time-frames after stimulus onset for subject s. Let area :Voxel fBA be a function, which
assigns to each voxel i  Voxel the corresponding fBA b  fBA. In a first pre-processing step,
we calculate for each subject s the value of 
the current density averaged over each fBA
(1)
xt , b;  , s  
1
Nb
 J t , i; , s 
ib
where Nb is the number of voxels in the fBA b, in condition  for subject s.
 In the second analysis stage, the mean current density x t,b;,s from each fBA b, for every
subject s and condition, was subjected to spatial PCA analysis of the correlation matrix and

varimax rotation
In the present study the spatial PCA uses the above-defined fBAs as variables sampled
along the time epoch for which EEG has been sampled (0-1000ms; 512 time-frames), and the
inverse solution was estimated. Spatial matrices (each matrix was sized b x t = 36 × 512
elements) for every subject and condition were collected, and subjected to PCA analyses,
including the calculation of the covariance matrix; eigenvalue decomposition and varimax
rotation, in order to maximize factor loadings. In other words, in the spatial PCA analysis we
approximate the mean current density for each subject in each condition as
(2)
xt; , s   x 0  , s    ck (t )x k  , s  ,
k
where here xt;,s  R36 is a vector, which denotes the time-dependent activation of the
fBAs, x 0  ,s is their mean activation, and x k  ,s and c k are the principal components and
 corresponding coefficients (factor loadings) as computed using the principal component
their
 analysis.


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