C. Long-term variability BCI performance is known to vary on multiple time scales. The quality of the presented new classification approach can only be rated in terms of its immediate applicability when the subjects are confronted with the specific feedback for the first time. By limiting the experimental sessions to a single day for each subject, we ensured that no long-term training effects can bias our experimental results. On the other hand, significant positive trends can be observed for static classifier setups when comparing performance across sessions, see e.g. [27]. Hence, we expect that the reiterated use of our classifiers on further experimental sessions will lead to results of a similar or even higher quality. Although the complex interaction process of subjects’ long-term adaptivity with this classifier is beyond the scope of this paper, note that this setup contains a largely static classifier based on specific brain signatures of particular subjects. It can be regarded as a promising starting point for further performance enhancements by operant conditioning. D. Adaptive Classification A possible remedy for the degrading performance is the adaptive estimation of the linear hyperplane of the classifiers, [17,28]. Using an adaptation period as short as 10 trials per class, however, the adaptation of the hyperplane for Zero-Training fails for almost every subject, as an offline evaluation on the given data shows. This is mainly due to the fact that for a linear classifier, the number of parameters to be estimated grows quadratically with the number of feature dimensions. Since the Zero-Training feature space has 12 dimensions (6 ‘‘prototype’’ filters and 6 ‘‘CSP’’ filters), 20 trials are too few data. Similar results have been shown in [17] for classical CSP; the suggested bias update requires only the estimation of one single parameter and is therefore more robust. If, however, the feature discrimination performance is changing over time like in subject zp, this bias update might not be sufficient any more. Other options, like a continuous adaptation of the bias throughout the feedback run, require at least the a posteriori knowledge of all the labels of this run, which can not be granted in all feedback applications. Moreover, this continuous adaptation scheme did not prove to be superior to the initial adaptation of the bias [18]. Fig. 8 suggests a good prediction accuracy for prototypical CSP filters with a low c-index. However, since the features of some subjects (e.g. zk and zp) appear to form distinct clusters for each class, we should consider some reasonable normalization between these values. The c-index, as formulated above, depends mainly on the number of dimensions and on the number of samples. This holds true because the maximally possible c-index is a monotonic decreasing function in the number of samples, if the number of dimensions (in this case: the number of electrodes) is fixed. Not only the maximal, but also the expected minimal c- index under randomly drawn samples will differ. Therefore, we estimated this value by a simulation: the number of dimensions and samples were chosen for every subject according to Table 1. The minimal c-value was calculated and averaged over 1000 repetitions. The results are displayed in Table 2. Since the values range from 1.12 for subject aw to 1.22 for subject ay, the correlation visualized in Fig. 8 is not influenced under the condition, that each c-value is normalized by the expected minimal c-value. Note that for subjects zk and ay, some of the c- values are close to 1 after normalization; this corresponds to a minimal ‘‘cluster’’ density which is expected to occur even in random samples. As expected, these features have only very low AUC-values. Towards Zero Training Table 2. This table shows the minimal c-index for a collection of randomly drawn points, together with the standard deviation. Subject Expected Minimal c zq ay zp al aw zk 1.1760.02 1.2260.02 1.2060.02 1.1560.02 1.1260.02 1.1760.02 For this calculation, the same dimensionality (corresponding to the number of electrodes) and the same number of points (corresponding to three times the number of experiments) was used. doi:10.1371/journal.pone.0002967.t002 With respect to the cumbersome electrode preparation great advancements could be achieved in the meantime by newly developed hardware. In [4] we present a novel dry EEG recording technology which does not need preparation with a conductive gel. The study with good BCI subjects revealed that the feedback performance using the new sensor technology was comparable to the approach with conventional EEG caps for most subjects. Note that the system reported in [4] only uses 6 electrodes and can thus be miniaturized to run with a tiny EEG amplifier and a pocket PC. E. Conclusion Brain-Computer Interfacing has seen a rapid development in the recent years. A main step forward towards a broader usability of this technology even beyond rehabilitation was the drastic reduction of user training from 60–150 hours of subject training to less than 30 minutes of calibration [29,2]. The latter became possible by virtue of modern machine learning methods for BCI [30,31]. In this contribution we went one step further towards the goal of avoiding subject training altogether and proposed novel algorithms to transfer knowledge between BCI sessions. Our study shows that the results from prior off-line analysis, successfully carry over to the present set of online experiments, where subjects use decoders that were constructed from past data instead of calibrating anew. Our findings thus show that information from prior session can indeed be used profitably for constructing better individual mental state decoders. Note that the loss in performance (bitrate) is negligible when contrasted to employing a fully calibrated decoder (after 30 minutes of training) in a blind protocol. Our work opens therefore a highly promising path for the ultimate goal of Zero-training. While the proposed methods work well for session to session transfer for an individual subject, it remains still open, whether inter-subject information could also be successfully transferred. Ideally a data base consisting of individualized decoders could be appropriately combined as an ensemble decoder and thus help to avoid training completely. In combination with dry electrodes [4], Zero-training would again provide a large step forward when striving towards more general applicability of BCI technology for daily use in man machine interaction. Author Contributions Conceived and designed the experiments: MK MT BB. Performed the experiments: MK MT. Analyzed the data: MK. Contributed reagents/ materials/analysis tools: MK BB. Wrote the paper: MK MT BB KRM. PLoS ONE | www.plosone.org 11 August 2008 | Volume 3 | Issue 8 | e2967