Towards Zero Training Figure 3. Overview of the 11 runs and the two methods used for calculating feedback. In block I, continuous visual feedback was given by a classifier that had been pre-computed with the Zero-Training method, see Section ‘‘Construction of Classifiers’’. Blocks II to V each contained one run with Zero-Training feedback and one run with ordinary CSP feedback. The order of the two feedback methods was chosen randomly and remained unknown to the subject. The use of continuous visual feedback and no continuous visual feedback alternated regularly between blocks II to V. doi:10.1371/journal.pone.0002967.g003 Table 1 for the imagery used by each subject), in order to hit the target with the cursor. Depending on the block (see Figure 3), the cursor was either visible during this cursor movement phase (continuous visual feedback) or a blank screen was presented for 3.5 seconds (no continuous visual feedback). The graded classifier output was used to control the cursor position in horizontal direction in a rate-controlled manner. After 3.5 seconds, the cursor was fixed again and the outcome of the trial was determined by the horizontal position of the cursor. If the cursor was on the correct side of the screen, the trial was counted as ‘‘hit’’, and as ‘‘missed’’ otherwise. The target box was then colored according to the trial outcome in green (for a successful trial) or red (in the other case). The highlighting of the target box at the end of the trial was visualized again for all trials, that means also for blocks where no continuous visual feedback was given. After a short intertrial break of 1 second the next target was presented. 4. Construction of Classifiers The following two sections describe how the spatial filters and classifier are determined for the proposed new approach and for the classical CSP approach. The feedback performance of these two approaches is compared using the experimental design described in Section ‘‘Experimental Setup’’. It will become clear, that both approaches will only use a small number of spatial filters (two or three per class) from the total set of filters provided by CSP. Although many more could be chosen in theory, experience with CSP for motor imagery paradigms has shown that further filters often model the noise of the data rather than the signals of interest. Thus the restriction to a small number of filters per class is helpful [10]. For a detailed discussion of the influence of data dimensionality on classification results please refer to the ‘‘Discussion’’ section. A. The Zero-Training Filters and Classifier. For each subject, data from a number of past sessions (past data) is available (see Table 1). Based on this data, a set of spatial filters and the Zero- Training classifier is constructed individually for each subject. This preparation could take place days before the planned feedback experiment, as only historic data is involved for the construction of Zero-Training. For every subject, we performed the following: For each class and for each historic session of the subject, we calculated the three filters with the largest eigenvalues using the CSP algorithm presented in Section ‘‘Common Spatial Patterns Analysis’’. Depending on the number of past sessions, this procedure creates a larger set of filters. Once this set of historic filters is created, 6 so-called prototype filters are chosen from the set applying the clustering method described in Section ‘‘Prototype Filters’’. Those filters constitute the first 6 dimensions of the final feature space for the Zero-Training method. In addition to these prototypical filters, we also pool all the data from past experiments of the subject of interest and calculated the ordinary CSP filters on this collection of historic data sets. The resulting CSP filters (3 per class) are concatenated to the 6 prototype filters gained from the clustering approach. With this approach, filtering the EEG data of the pooled data set (all past sessions of the subject) results in a 12-dimensional feature space. Finally, a linear classifier is calculated on the features using Least Squares Regression (LSR). If necessary we could also use nonlinear classification here (cf. [22,23]). B. The Ordinary CSP Filters and Classifier. For each subject, we also build a set of ordinary CSP filters and a corresponding classifier. In contrast to the Zero-Training solution, they can not be prepared beforehand. Their construction is done on the fly during a new experimental session and does not involve data from past sessions. For the training of a regular CSP classifier, we first record three runs of feedback data (with feedback provided by the output of the Zero-Training classifier), totalling to more than 150 trials per class. According to the cross-validation error on this data, the optimal frequency band is selected, as well as some additional parameters like length and starting point of the training time interval for estimating the band power. The Common Spatial Patterns are computed on this data and the two spatial filters representing the most extreme eigenvalues are chosen for each class. Then a linear classifier (LSR) was trained using the prepro- cessed data from the first three runs. Results A. Feedback Performance The first three runs of feedback showed that all subjects under study were able to operate the BCI with the pre-computed classifier at a high accuracy (only 10 trials per class from the current day were required to update the bias for the classification scenario). For every subject Fig. 4 shows the percentage of PLoS ONE | www.plosone.org 5 August 2008 | Volume 3 | Issue 8 | e2967