The suitable amount of regularization may depend on the variance of the data. You may need to try different values to find the one that suits your situation best.

Before the CSP training, it may be useful to temporally filter the input data to remove bands which are believed to have no relevant discriminative information.

Note that the usage of the CSP filters before classification training can make the cross-validation results optimistic, unless strictly non-overlapping

parts of the data were used to train the CSP and the classifier (disjoint sets for each).

Note that the usage of the CSP filters before classification training can make the cross-validation results optimistic, unless strictly non-overlapping parts of the data were used to train the CSP and the classifier (disjoint sets for each).

The origin of the trace normalization trick has been lost. Presumably the idea is to normalize the scale of

each chunk in order to compensate for possible signal power drift over time during the EEG recording,

The trace normalization can be found in the literature [2]. Presumably the idea is to normalize the

scale of each chunk in order to compensate for a possible signal power drift over time during the EEG recording,

making each chunks' covariance contribute similarly to the aggregate regardless of the current chunks average power.

To get the "CSP with Diagonal Loading" of Lotte & Guan paper, set shrinkage to a positive value and Tikhonov to 0. To get the

"CSP with Tikhonov regularization", do the opposite. You can also try a mixture of the two. Note that the Guan & Lotte paper does not use trace normalization.

To get the "CSP with Diagonal Loading" of Lotte & Guan paper [1], set shrinkage to a positive value and Tikhonov to 0. To get the

"CSP with Tikhonov regularization", do the opposite. You can also try a mixture of the two. Note that the Guan & Lotte paper does not appear to use trace normalization.

Once the spatial filters are computed and saved in the configuration file, you can load the configuration into the \ref Doc_BoxAlgorithm_SpatialFilter "Spatial Filter" box.

For the moment the Regularized CSP Trainer supports only two classes.

References

1) Lotte & Guan: "Regularizing common spatial patterns...", 2011.

2) Muller-Gerkin & al., "Designing optimal spatial filters for single-trial EEG classification in a movement task", 1999.

3) Chan, Golub & Leveq, "Updating formulae and a pairwise algorithm...", 1979.