Towards Zero Training Figure 7. The effect of the bias estimation for subject zp (see text for discussion). In the left part of the figure, both Zero-Training and the original CSP-classifier are computed on the 1100 trials of the feedback session, without adding a bias term. While the CSP method performs already quite well, the output of Zero-Training (on the y-axis) is negative for almost all samples, which would correspond to a classification error near 50%. The right part of the figure shows the output on the same trials, after an initial bias adaptation on the 20 initial trials per run. For the CSP classification, the bias is not changing the result significantly, but Zero-Training clearly profits from the bias update. doi:10.1371/journal.pone.0002967.g007 could be performed. We evaluated the performance of our new classifier by comparing it to the standard CSP method that is typically used for the classification of band power features in motor imagery paradigms (see e.g. [3]). In order to keep the subjects equally motivated under both conditions, we changed the classifier options randomly between runs, but did not inform subjects about the currently used classifier. They were instructed to keep trying to hit the targets given on the screen, irrespective of the possibly fluctuating performance. The aim of this study was to construct and evaluate a classification method that can be applied without a lengthy calibration measurement. While the features we chose have proven to be quite discriminative for the classification task at hand, the bias adaptation was indispensable for two of the six subjects (and did not degrade the performance for the other subjects). Possible explanations for the shift of the bias from one session to another include the differences in electrode impedances as well as physiological effects like superimposed occipital a-rhythm, see [18,17,25]. The number of trials per class used for the adaptation period has to be chosen according to a trade-off between the total duration of the adaptation period and the precision of the bias estimation. After preliminary off-line evaluations we found 10 trials per class to be a quite balanced choice. Note that this number might as well be adjusted according to the predicted feedback accuracy for the subject. Bias parameter estimation is clearly expected to degrade with stronger feedback variance during the adaptation period, and our findings support this expectation. Therefore, if a low feedback performance is expected for a subject, one can easily increase the number of trials used for adaptation. On the other hand the total duration of the adaptation period should be kept very short, since it is desirable for a real- world BCI application to operate right from the start. In such a situation knowledge about class labels is not available and even the equal probability for the occurrence of class labels is not always a reasonable assumption. In this study, the training data for the CSP-classifier are different from the usual calibration data: in the standard case, no feedback is given during the presentation of stimuli. Also, the visual scene now resembles more closely the feedback setup (see [18]), i.e., the targets are on the left and right side of the screen and they change their color to indicate the next movement task. Although one might suspect that this could degrade the classification performance of the CSP classifier due to the higher complexity of the mental task, this is not the case. Fig. 9 shows the development of the cross-validation error over the previous experiments for each subject. Parameters like the frequency band and the time interval were optimized specifically for each subject and each session. The last point (session N) denotes the online experiment performed for this study, where the first three feedback runs were taken into account for training. This corresponds to the data on which the CSP classifier was trained. The cross-validation performance for this session is on the same level as the previous performance and hence does not reveal a systematic disadvantage for the CSP method. On the contrary, the following application of the classifier might even benefit from the fact that the task difference between the training data and the test data is relatively small, as both task have an increased visual complexity. PLoS ONE | www.plosone.org 8 August 2008 | Volume 3 | Issue 8 | e2967