Classification accuracy for the calibration data of three conditions.

<p>The binary classification accuracy, estimated with cross validation is plotted for each condition and subject (A). The thick black line marks the mean. Plots <b>B</b> depicts the multi-class accuracy for the two classification approaches (“std” and “meta”). This was estimated by cross-validation on calibration data, using entire trials as test sets. Precisely, the point for rank = <i>i</i> quantifies the fraction of trials with a rank of the target class equal or lower than <i>i</i>. Thus, the mean multi-class performance (correct decision – rank = 1) was 47% (41%) for the meta (std) classifier. One can observe that 77% (72%) of the trials have a multi-class rank better or equal than 5. While perfect BCI control (each 30-class decision is correct) would result in a straight line with y = 1, the dashed line marks the multi-class accuracy based on chance level.