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Detecting Mental States by Machine Learning Techniques 121 about half a second after the indication of the target. The trial ended when the cursor touched one of the two fields. That field was then colored green or red, depending on whether or not it was the correct target. After a short period the next target cue was presented (see [7, 42] for more details). The aim of our first feedback study was to explore the limits of possible information transfer rates (ITRs) in BCI systems without relying on user training or evoked potentials. The ITR derived in Shannon’s information theory can be used to quantify the information content, which is conveyed through a noisy (i.e., error introducing) channel. In BCI context, this leads to bitrate(p, N) = � p log 2 (p) + (1 − p)log 2 � � � 1 − p + log2 (N) , (3) N − 1 where p is the accuracy of the user in making decisions between N targets, e.g., in the feedback explained above, N = 2 and p is the accuracy of hitting the correct bars. To include the speed of decision into the performance measure, we define ITR [bits/min] = # of decisions · bitrate(p, N). (4) duration in minutes In this form, the ITR takes different average trial durations (i.e., the speed of decisions) and different number of classes into account. Therefore, it is often used as a performance measure of BCI systems [43]. Note that it gives reasonable results only if some assumptions on the distribution of errors are met, see [44]. The participants of the study [7, 14] were six staff members, most of whom had performed feedback with earlier versions of the BBCI system before. (Later, the study was extended by four further volunteers, see [42]). First, the parameters of preprocessing were selected and a classifier was trained based on a calibration individually for each participant. Then, feedback was switched on and further parameters of the feedback were adjusted according to the user’s request. For one participant, no significant discrimination between the mental imagery conditions was found; see [42] for an analysis of that specific case. The other five participants performed eight runs of 25 cursor control trials as explained above. Table 1 shows the performance results as accuracy (percentage of trials in which the user hit the indicated target) and as ITR (see above). As a test of practical usability, participant al operated a simple text entry system based on BBCI cursor control. In a free spelling mode, he spelled three German sentences with a total of 135 characters in 30 min, which is a spelling speed of 4.5 letters per minute. Note that the participant corrected all errors using the deletion symbol. For details, see [45]. Recently, using the novel mental text entry system Hex-o-Spell, which was developed in cooperation with the Human-Computer Interaction Group at the University of Glasgow, the same user achieved a spelling speed of more than 7 letters per minute, cf. [1, 46, 47].
122 B. Blankertz et al. Table 1 Results of a feedback study with six healthy volunteers (identification code in the first column). From the three classes used in the calibration measurement, the two chosen for feedback are indicated in the second column (L: left hand, R: right hand, F: right foot). The accuracies obtained online in cursor control are given in column 3. The average duration ± standard deviation of the feedback trials is provided in column 4 (duration from cue presentation to target hit). Participants are sorted according to feedback accuracy. Columns 5 and 6 report the information transfer rates (ITR) measured in bits per minute as obtained by Shannon’s formula, cf. (3). Here, the complete duration of each run was taken into account, i.e., also the inter-trial breaks from target hit to the presentation of the next cue. The column overall ITR (oITR) reports the average ITR of all runs (of 25 trials each), while column peak ITR (pITR) reports the peak ITR of all runs id classes accuracy [%] duration [s] oITR [b/m] pITR [b/m] al LF 98.0 ± 4.3 2.0 ± 0.9 24.4 35.4 ay LR 95.0 ± 3.3 1.8 ± 0.8 22.6 31.5 av LF 90.5 ± 10.2 3.5 ± 2.9 9.0 24.5 aa LR 88.5 ± 8.1 1.5 ± 0.4 17.4 37.1 aw RF 80.5 ± 5.8 2.6 ± 1.5 5.9 11.0 mean 90.5 ± 7.6 2.3 ± 0.8 15.9 27.9 3.2 Good Performance Without Subject Training The goal of our second feedback study was to investigate which proportion of naive subjects could successfully use our system in the very first session [48]. The design of this study was similar to the one described above. But here, the subjects were 14 individuals who never performed in a BCI experiment before. feedback accuracy [%] 90 80 70 60 50 cm ct cp zp cs cu ea at zr co eb cr cn 90−100 80−90 70−80 60−70
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THE FRONTIERS COLLECTION
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Bernhard Graimann · Brendan Alliso
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Preface It’s an exciting time to
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Contents Brain-Computer Interfaces:
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Contributors Brendan Allison Instit
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Contributors xi Femke Nijboer Insti
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List of Abbreviations ADHD Attentio
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Brain-Computer Interfaces: A Gentle
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30 J.R. Wolpaw and C.B. Boulay by b
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32 J.R. Wolpaw and C.B. Boulay 2 Br
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34 J.R. Wolpaw and C.B. Boulay Fig.
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36 J.R. Wolpaw and C.B. Boulay Sens
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38 J.R. Wolpaw and C.B. Boulay pote
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40 J.R. Wolpaw and C.B. Boulay EEG-
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42 J.R. Wolpaw and C.B. Boulay 29.
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Non Invasive BCIs for Neuroprosthes
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BCIs Based on Signals from Between
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260 J. Mellinger and G. Schalk mapp
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262 J. Mellinger and G. Schalk 2 BC
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322 Y. Li et al. is small and the n
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324 Y. Li et al. (ROC) analysis app
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326 Y. Li et al. Fig. 10 The stimul
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Toward Ubiquitous BCIs Brendan Z. A
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Toward Ubiquitous BCIs 359 BCI Chal
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Toward Ubiquitous BCIs 361 communic
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Toward Ubiquitous BCIs 363 facial m
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Toward Ubiquitous BCIs 365 The best
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Toward Ubiquitous BCIs 367 Across a
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Toward Ubiquitous BCIs 369 the ques
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Toward Ubiquitous BCIs 371 controll
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Toward Ubiquitous BCIs 373 controls
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Toward Ubiquitous BCIs 375 hair in
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Toward Ubiquitous BCIs 377 Table 1
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Toward Ubiquitous BCIs 379 conversa
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Toward Ubiquitous BCIs 381 may down
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Toward Ubiquitous BCIs 383 physicis
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Toward Ubiquitous BCIs 385 31. F.H.
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Toward Ubiquitous BCIs 387 67. G. S
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390 Index 65, 157, 163, 217, 221-23
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392 Index 208, 236, 243, 245, 260-2
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