GeezerVison

The eigenvectors composing $𝐏$ are components with variance ratio between the two windows equal to their corresponding eigenvalue:

${𝝀}_i=\frac{{\Vert {𝐩}_i^{\text{T}}{𝐗}_1\Vert }^2}{{\Vert {𝐩}_i^{\text{T}}{𝐗}_2\Vert }^2}$

The vectorial subspace $E_i$ generated by the $i$ first eigenvectors $\left[\begin{array}{ccc}{𝐩}_1& \cdots & {𝐩}_i\end{array}\right]$ will be the subspace maximizing the variance ratio of all components belonging to it:

$E_i={\arg max}_E\left(\begin{array}{c}{min}_{p\in E}\frac{{\Vert {{𝐩}^{\text{T}}𝐗}_1\Vert }^2}{{\Vert {{𝐩}^{\text{T}}𝐗}_2\Vert }^2}\end{array}\right)$

On the same way, the vectorial subspace $F_j$ generated by the $j$ last eigenvectors $\left[\begin{array}{ccc}{𝐩}_{n-j+1}& \cdots & {𝐩}_n\end{array}\right]$ will be the subspace minimizing the variance ratio of all components belonging to it:

$F_j={\arg min}_F\left(\begin{array}{c}{max}_{p\in F}\frac{{\Vert {{𝐩}^{\text{T}}𝐗}_1\Vert }^2}{{\Vert {{𝐩}^{\text{T}}𝐗}_2\Vert }^2}\end{array}\right)$

CSP can be applied after a mean subtraction (a.k.a. "mean centering") on signals in order to realize a variance ratio optimization. Otherwise CSP optimizes the ratio of second-order moment.

• The standard use consists on choosing the windows to correspond to two periods of time with different activation of sources (e.g. during rest and during a specific task).
• It is also possible to choose the two windows to correspond to two different frequency bands in order to find components with specific frequency pattern.^[2] Those frequency bands can be on temporal or on frequential basis. Since the matrix $𝐏$ depends only of the covariance matrices, the same results can be obtained if the processing is applied on the Fourier transform of the signals.
• Y. Wang ^[3] has proposed a particular choice for the first window ${𝐗}_1$ in order to extract components which have a specific period. ${𝐗}_1$ was the mean of the different periods for the examined signals.
• If there is only one window, ${𝐑}_2$ can be considered as the identity matrix and then CSP corresponds to Principal component analysis.

Linear discriminant analysis (LDA) and CSP apply in different circumstances. LDA separates data that have different means, by finding a rotation that maximizes the (normalized) distance between the centers of the two sets of data. On the other hand, CSP ignores the means. Thus CSP is good, for example, in separating the signal from the noise in an event-related potential (ERP) experiment because both distributions have zero mean and there is no distinction for LDA to separate. Thus CSP finds a projection that makes the variance of the components of the average ERP as large as possible so the signal stands out above the noise.