Brainâ€“Computer Interfaces - Index of

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The First Commercial Brain–Computer Interface Environment 297 Fig. 13 Simulink model for P300 analysis and smart home control each flashing event and sends the “ID” to the “Signal Processing” block. The “Signal Processing” block creates a buffer for each character. After all the characters flashed, the EEG data is used as input for the LDA and the system decides which letter was most likely investigated by the subject. Then this character is displayed on the computer screen. Nowadays, the P300 concept allows very reliable results with high information transfer rates of up to 1 character per second [7, 21, 22]. 4 BCI Applications 4.1 IntendiX intendiX R○ is designed to be installed and operated by caregivers or the patient’s family at home. The system consists of active EEG electrodes to avoid abrasion of the skin, a portable biosignal amplifier and a laptop or netbook running the software under Windows (see Fig. 14). The electrodes are integrated into the cap to allow a fast and easy montage of the intendiX equipment. The intendiX software allows viewing the raw EEG to inspect data quality, but indicates automatically to the unexperienced user if the data quality on a specific channel is good or bad. If the system is started up for the first time, a user training has to be performed. Therefore usually 5–10 training characters are entered and the user has to copy spell the characters. The EEG data is used to calculate the user specific weight vector which is stored for later usage. Then the software switches automatically into the spelling mode and the user can spell as many characters as wanted. The user can perform different actions: (i) copy the spelled text into an Editor, (ii) copy the text into an email, (iii) send the text via text-to-speech facilities to the
298 C. Guger and G. Edlinger Fig. 14 Intendix running on the laptop and user wearing the active electrodes Fig. 15 User interface with 50 characters and computer keyboard like layout loud speakers, (vi) print the text or (v) send the text via UDP to another computer. For all these services a specific icon exists as shown in Fig. 15. The number of flashes for each classification can be selected by the user or the user can also use a statistical approach that detects automatically if the user is working with the BCI system. The latter one has the advantage that no characters are selected if the user is not looking at the matrix or does not want to use the speller. 4.2 Virtual Reality Smart Home Control with the BCI Recently, BCI systems were also combined with Virtual Reality (VR) systems. VR systems use either head mounted displays (HMDs) or highly immersive
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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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48 G. Pfurtscheller and C. Neuper F
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52 G. Pfurtscheller and C. Neuper r
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54 G. Pfurtscheller and C. Neuper 6
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56 G. Pfurtscheller and C. Neuper B
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66 C. Neuper and G. Pfurtscheller s
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68 C. Neuper and G. Pfurtscheller 2
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70 C. Neuper and G. Pfurtscheller F
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72 C. Neuper and G. Pfurtscheller b
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74 C. Neuper and G. Pfurtscheller E
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80 G. Pfurtscheller et al. mode, th
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82 G. Pfurtscheller et al. Therefor
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84 G. Pfurtscheller et al. A B C 1
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86 G. Pfurtscheller et al. filters
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88 G. Pfurtscheller et al. differen
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90 G. Pfurtscheller et al. In this
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92 G. Pfurtscheller et al. Fig. 8 P
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94 G. Pfurtscheller et al. 10. D. F
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96 G. Pfurtscheller et al. 51. G. P
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98 E.W. Sellers et al. Fig. 1 Three
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100 E.W. Sellers et al. Fig. 3 Two-
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102 E.W. Sellers et al. Fig. 5 Comp
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104 E.W. Sellers et al. Fig. 7 Mont
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106 E.W. Sellers et al. fixation wa
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108 E.W. Sellers et al. 5 SMR-Based
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110 E.W. Sellers et al. 22. D.J Kru
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Detecting Mental States by Machine
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138 Y. Wang et al. which has been e
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140 Y. Wang et al. After many studi
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142 Y. Wang et al. Left Hand Right
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144 Y. Wang et al. 0-degree 60-degr
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146 Y. Wang et al. 3.1.2 Stimulatio
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148 Y. Wang et al. Foot 1 0.5 Left
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150 Y. Wang et al. be summarized as
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152 Y. Wang et al. Fig. 10 A player
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154 Y. Wang et al. 22. Y. Wang, R.
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156 N. Birbaumer and P. Sauseng C A
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158 N. Birbaumer and P. Sauseng 3 B
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160 N. Birbaumer and P. Sauseng pat
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162 N. Birbaumer and P. Sauseng Fig
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164 N. Birbaumer and P. Sauseng of
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166 N. Birbaumer and P. Sauseng Neu
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168 N. Birbaumer and P. Sauseng 8.
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Non Invasive BCIs for Neuroprosthes
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Brain-Computer Interfaces for Commu
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204 D.M. Taylor and M.E. Stetner mo
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206 D.M. Taylor and M.E. Stetner el
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208 D.M. Taylor and M.E. Stetner ac
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210 D.M. Taylor and M.E. Stetner de
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212 D.M. Taylor and M.E. Stetner Ma
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214 D.M. Taylor and M.E. Stetner pe
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216 D.M. Taylor and M.E. Stetner ev
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218 D.M. Taylor and M.E. Stetner fo
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BCIs Based on Signals from Between
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242 K.J. Miller and J.G. Ojemann 2
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244 K.J. Miller and J.G. Ojemann ha
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348 A. Schlögl et al. Error [%] RL
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350 A. Schlögl et al. Table 3 Aver
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352 A. Schlögl et al. The extended
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354 A. Schlögl et al. 24. N.G. Pfu
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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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