Towards Zero Training Figure 8. This figure compares the c-index of a prototypical CSP filter, as calculated on previous sessions, with the discriminability of this feature in the feedback session. The filters with the lowest c-index have the highest performance. This correlation is highly significant (p,0.01). doi:10.1371/journal.pone.0002967.g008 A. About Dimensionality The Zero-Training method and standard CSP were used with different data dimensions (12 and 4 resp.). While the absolute numbers might not be very relevant, we would like to explain the motivations that lead to this rather unequal choice: For the standard CSP method, it seems reasonable to expect that it results with spatial filters that are well-adapted to the data of the current session. This is a good argument for fixing the number of spatial filters (and thus the data dimension) to a smaller number. The Zero-Training method on the other hand might is dependent on a richer and more robust basis of spatial filters, as the current session might differ from some or most of the historic sessions. Providing the new method with altogether 12 historic filters enhances the probability that one of them is informative also for the new session. It is an interesting open question, whether the larger basis for Zero-Training biases the comparison with a systematic disadvantage for the standard CSP method. To investigate, whether standard CSP can profit from a richer basis using more filters, we conducted an offline comparison of classification performance. Here the Zero-Training method was fixed to 12 dimensions and compared to standard CSP method with varying dimensions between 4 and 12. However, the offline performance of standard CSP (as a variable of CSP’s dimension- ality) showed only little variation. It was in the same range as the systematic variance induced by the error estimation technique itself during different cross-validation folds. Given the perfor- mance of Zero-Training and CSP at eye level end (as shown in Section ‘‘Results’’) and furthermore that standard CSP can not profit from enlarged dimensionality, this argues in favour of the robust design of our new Zero-Training method. PLoS ONE | www.plosone.org 9 August 2008 | Volume 3 | Issue 8 | e2967