Multifocal visual evoked potentials (mfVEP) were recorded with three channels from 31 control subjects. A principal component analysis was applied to all local responses. The first principal component reversed polarity above and below the horizontal meridian in the case of the midline channel and across the vertical meridian in the case of the lateral channel. In addition, the first principal components of the responses around the vertical meridian were reversed in polarity compared to those around the horizontal meridian, consistent with the region near the vertical meridian lying outside the calcarine fissure. A model was proposed that allowed for the construction of a coronal section of V1 based on the distribution of the first principal component. This approach provides a means of deriving a V1 component from mfVEP recordings with only three recording channels.

*M*×

*N*matrix

**A**can be decomposed into a product of an

*M*×

*N*column-orthogonal matrix

**U**, an

*N*×

*N*diagonal matrix

**W**with positive or zero elements (the singular values) and an

*N*×

*N*orthogonal matrix

**V**(Equation 1). Equation 2 is the formula for applying the SVD algorithm to the VEP data, where

*M*is the number of points of a VEP trace, N is the number of all the VEP (e.g., the number of recording channels multiplied by the number of locations). An item in matrix

**A**,

_{ij}is the

*i*

^{th}point of response

*j*, an item in matrix

**U**,

_{ij}is the

*i*

^{th}point of PC

*j*, an item in matrix

**V**and,

*c*

_{ij}is the coefficient of the

*i*

^{th}PC for reconstructing the

*j*

^{th}response. The relative importance of the

*i*

^{th}principal component is represented as

_{ii}in the diagonal values of a

*N*-by-

*N*diagonal matrix

**W**. Usually a small number

*L*(e.g.

*L*= 2 or

*L*= 3) of PCs are sufficient to reconstruct all the VEP responses (Equation 3). The rest of the PCs contain mainly noise and should be discarded.

*A*is a 180-by-408 matrix. The dimension along the points in responses is referred to as the temporal domain, and the dimension along the location and channels is referred to as the spatial domain.

^{ii}) of matrix

**W**, and a matrix of original coefficients. Both the original PCs (matrix

**U**) and the original coefficients (matrix

**V**) are normalized to unit magnitude. The value

_{ii}

^{2}can be interpreted as the power of the

*i*

^{th}PC. The percentage of variance in all data that can be accounted for by the first

*L*PCs is calculated with Equation 4. This value also serves as the goodness of fit index for the PCA in this work.

_{ii}is used as the estimation of the average amplitude of the

*i*

^{th}PC. To represent the relative amplitude of each PC, the waveform of a PC is multiplied by the correspondent

*w*value. In other words, a PC presented in this work is actually the original PC multiplied by its

*w*value.