Schreuder et al. Listen, you are writing! FIGURE 4 | Accuracy over time. Selection accuracy of Session 1 is plotted for each subject as a function of the word written, which approximates time. The gray line represents the mean over the subjects that ﬁnished the entire sentence, which shows no signiﬁcant slope. The red dotted line represents the mean over all subjects. session. For each sentence, the online classiﬁcation scores of the ﬁrst four iterations were used to take a decision (the minimum number of iterations in Session 2 was 4). The red, dotted line in Figure 3A shows these results. No systematic learning effect was present. As sentence 1 (m = 0.88, SD = 0.07) and sentence 2b showed no signiﬁcant performance difference (p = 0.40), it can thus be concluded that early stopping did not introduce addi- tional errors. Statistical testing was done using two-sided paired t -tests. Figure 4 shows the accuracy for each subject as a function of the word written in the ﬁrst sentence, which we take as an approx- imation of time. Considering only those subjects that were able to ﬁnish the session, the slope of the linear least-squares ﬁt between performance and the word number is close to 0, implying that subjects are able to use the interface straight away. The average ITR over all 21 subjects of Session 1 was 2.84 bits/min (maximum 4.59 bits/min), which includes also sub- jects that did not reach a performance level appropriate for writing (see Table 2). When considering only those subjects that per- formed well on all sentences, the average ITR over Session 1 was 3.75 bits/min. During the second session this increased by 40% to an average of 5.26 bits/min, with a maximum of 7.55 bits/min. WRITING EFFICIENCY Figure 3B presents the writing proﬁciency for each subject and each session in terms of characters per minute (char/min). The black line represents the sentence-wise mean for all subjects who ﬁnished that sentence. The gray line represents the sentence-wise mean for only those subjects that ﬁnished all sentences (N = 14), which will be considered for further analyses. As expected from the accuracy, there is no signiﬁcant difference between sentence 2a (m = 0.91, SD = 0.25) and sentence 2b (m = 0.94, SD = 0.31) of Session 2 (p = 0.57). However, when comparing sentence 1 (m = 0.62, SD = 0.16) with sentence 2b, a signiﬁcant increase of 52% can be observed (t = 4.68; p < 0.001). As the accuracy was equal, the increase must be due to improvements in the inter- face’s efﬁciency. These were twofold: (1) dynamic stopping was introduced and (2) the vocalization of the labels was omitted. To assess the inﬂuence of the dynamic stopping method inde- pendently, the 6.2-s time gain from the second modiﬁcation was added again to sentence 2b in an ofﬂine simulation. A signiﬁ- cant increase of 37% remains from sentence 1 to the simulated sentence 2b (m = 0.85, SD = 0.26; t = 4.05; p = 0.001). Thus, the dynamic stopping method increases performance signiﬁcantly by itself. Statistical testing was done using two-sided paired t -tests. DYNAMIC STOPPING METHOD Over all subjects, a total of 3297 online trials were recorded in Session 2. The dynamic method produced early stops in 41.3% of these trials. The maximum number of 15 iterations (full trial) was reached in the remaining 58.7% of the trials. This was in cases where the threshold was not exceeded in iterations 4–14. Early stops before the fourth iteration were not allowed. About 12.6% of early stops resulted in an incorrect deci- sions, which is only 5.2% of the overall number of trials. This reﬂects the conservative threshold policy as described in the meth- ods. At 23.5%, the percentage of errors was almost twice as high for full trials. An obvious explanation is that mostly the “difﬁcult-to-decide-trials” reach the full number of iterations. As depicted in Figure 5A, the majority of early stops is per- formed directly at the fourth iteration, with the number of stops decreasing until ﬁnally remaining trials are stopped at the 15th trial. At each individual iteration the percentage of incorrect decisions is lower than for full trials. Figure 5B gives the different errors for each subject and sen- tence 2a and 2b. It shows that the error rate for early stops is relatively low, and similar for all subjects. Only subject VPfch had such a high error rate – both for early stops and full trials – that there was no time left for writing the second sentence. The black lines indicate the minimum number of decisions necessary for writing the particular sentence. PHYSIOLOGY Calibration data from the ﬁrst session of all subjects was pooled and the grand-average calculated. Artifacts were rejected accord- ing to a ﬁxed variance criterium and a peak-to-peak difference criterium. Figure 6A shows the grand-average time series for channel Cz. An early component is consistently present, peaking about 130 ms after each stimulus. This causes a rhythmic pertur- bation of the EEG signal, thereby masking the generally described ERP components. Although present in both target and non-target trials, it is more negative for target trials, i.e., it is attention mod- ulated. An interval from 95 to 200 ms was selected to capture this peak. A positive component is found for target trials, captured in the interval from 280 to 450 ms. Due to both timing and scalp location, we consider this to be what is described as the P3 component. Figure 6B shows the grand-average scalp topography for both target and non-target trials for both selected intervals, whereas Figure 6C displays the discriminability between those, expressed in signed AUC values (Schreuder et al., 2010). Low voltage ampli- tude and AUC values are due to inter subject differences. Online data are similar and thus not presented here. It is apparent that the early negative component is found mainly over the frontal www.frontiersin.org October 2011 | Volume 5 | Article 112 | 7