between LDA and CSP 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. Applications The CSP method can be applied to multivariate signals in generally, is commonly found in application to electroencephalographic (EEG) signals. Particularly, the method is often used in brain–computer interfaces to retrieve the component signals which best transduce the cerebral activity for a specific task (e.g. hand movement). It can also be used to separate artifacts from EEG signals. CSP can be adapted for the analysis of the event-related potentials. See also Blind signal separation References Signal processing