Figure 9. The discriminability of the calibration data for each previous session (N27,…,N21) as calculated by the cross- validation error of the CSP algorithm. Frequency band and time window were specifically optimized for each session and each subject. The cross-validation error on session N is calculated on the three runs from block I, with the settings shown in table 1. doi:10.1371/journal.pone.0002967.g009 B. Online Performance and Quality of Adaptation It has been shown in recent publications [26,10], that the optimization of spatial and temporal parameters can result in a significantly increased classification accuracy. For the training of the Zero-Training classifier however, some of the parameters were not specifically optimized, such as the frequency band, the training window for parameter estimation on the previous sessions, and the Towards Zero Training movement type combination used for the feedback. These parameters were fixed beforehand. In contrary to this, the subject-dependent parameters of the standard CSP method were selected individually based on the same day’s training data. We are fully aware, that this comparison strategy may have resulted in a slight advantage in favor of the standard CSP method, but we accepted this advantage in order to have a maximally strong adversary method available for the comparison with our new Zero- Training method. Only in subject zk, the CSP classifier clearly outperforms the Zero-Training classifier. The reason might be due to the smaller amount of training data which was present for zk from previous sessions: while the training sessions for all other subjects contained more than 100 trials per class, only 35 trials per class and session were recorded for subject zk, see also table 1. This circumstance leads to a higher variability in the collection of CSP filters and it explains the low c-index for all features of subject zk, see Fig. 8. For subject zk, the c-values for the Zero-Training features are slightly higher than for subject zp. From the feedback performance in Fig. 4, we can even see a slow positive trend for the Zero-Training classifier throughout the day. The trend in the performance for the CSP classifier, on the other hand, is degrading over time. Subject zp reported that she was trying to control the feedback with different strategies over time, always switching to the mental imagery that seemed most reliable at each point in time. This variability in the mental strategies, induced by the feedback presentation, is reflected in the brain signals. Fig. 10 shows the evolution of the scalp topographies related to the discriminability of the band power features in each electrode. We calculated the band power features for the 100 feedback trials in each run and calculated the r2-values between left and right hand imagery class, as a measure of linear discriminability. The figure shows that towards the end of the session, the features on the right motor cortex are more discriminative than the features initially on the left motor cortex. The feedback performance of the CSP classifier appears to be more susceptible to this shift, while the Zero-Training classifier is based on a broader basis of spatial filters, which can account for this variability in the signals. Figure 10. For each feedback run of the session, this figure shows the scalp topographies of class discriminability of band power features for subject zp. After bandpass filtering to the frequency band of 10–25 Hz, the log-bandpower was calculated for each electrode in the window 500–3000 ms after the presentation of the stimulus. Finally, signed r2-values were calculated as a measure of class discriminability. doi:10.1371/journal.pone.0002967.g010 PLoS ONE | www.plosone.org 10 August 2008 | Volume 3 | Issue 8 | e2967