Brainâ€“Computer Interfaces - Index of

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Toward Ubiquitous BCIs Brendan Z. Allison 1 Introduction The preceding chapters in this book described modern BCI systems. This concluding chapter instead discusses future directions. While there are some specific predictions, I mainly analyze key factors and trends relating to practical mainstream BCI development. While I note some disruptive technologies that could dramatically change BCIs, this chapter focuses mainly on realistic, incremental progress and how progress could affect user groups and ethical issues. There are no other BCI articles like this chapter. Some articles address BCIs further in the future [68], or mention future directions en passant [11, 59, 60, 74], but this chapter focuses largely on the mechanics of current and foreseeable change. BCIs are in the early stages of a slow but major revolution that will change BCIs from mostly impractical and inaccessible exotica for very specific user groups into increasingly ubiquitous tools. This view is not conventional. People typically extrapolate a dim future for BCIs, since their views are based on current systems and user groups. BCIs exist primarily to enable communication for people who cannot communicate otherwise due to severe motor disability. Thus, BCIs are not useful to people who can communicate through other means. I encounter this conventional perspective all the time, whether talking to other research scientists, media or business experts, or laypeople. It may be influenced by science fiction, which typically presents BCIs as ubiquitous and useful, but only because of advances far beyond conceivable technology. This perspective assumes that: 1. BCIs only provide communication and control 2. BCIs provide the same information otherwise available through other interfaces 3. BCIs are exclusive interfaces B.Z. Allison (B) Institute for Knowledge Discovery, Laboratory of Brain–Computer Interfaces, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria e-mail: allison@tugraz.at B. Graimann et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection, DOI 10.1007/978-3-642-02091-9_19, C○ Springer-Verlag Berlin Heidelberg 2010 357
358 B.Z. Allison It follows that BCIs are of no practical value to people who can otherwise communicate, and will not become more widely adopted unless they can become much faster. This perspective is mistaken. Instead, BCIs may provide useful communication to many new user groups, including persons with less severe movement disabilities and healthy people. The very definition of a BCI as a tool for communication and control is beginning to change. BCIs might also provide other functions, such as rehabilitation of different disorders [59, 60] (see also chapters “The Graz Brain– Computer Interface” and “Brain–Computer Interface in Neurorehabilitation” in this book), which are either unattainable or problematic for other interfaces. Furthermore, the critical progress that is needed is not in speed but other key factors like improved sensors, greater accessibility to nonexperts, and smoother integration with existing devices and software. These and some other key factors are progressing at a rapid and potentially revolutionary pace. BCIs will therefore become practical tools for much wider user groups and goals than most people think. BCIs will not become ubiquitous in the next 10 years, but they will grow well beyond communication for severely disabled users. 2 Key Factors in BCI Adoption Schalk [68] discusses BCI adoption much like adoption of other consumer electronics, such as radio, television, or DVDs. This approach, while unconventional within the BCI research community, is becoming increasingly popular with other EEG-based applications such as neuromarketing, lie detection, and sleep or alertness monitoring. These applications are not BCIs because they do not provide realtime feedback and/or do not allow intentional communication (see also chapter “Brain– Computer Interfaces: A Gentle Introduction”). However, progress with these and related systems could affect BCI development. Schalk [68] depicts the relationship between BCI price and performance, by which Schalk means bits/second. While a BCI’s information transfer rate (ITR) is important, other factors may be at least as important to potential users [11, 36, 83]. The right table in Fig. 1 below summarizes these key factors. Each of these factors is influenced by both hardware and software considerations. The two tables on the left side of Fig. 1 present three catalysts unique to BCIs and several related catalysts. The arrows indicate that these two types of catalysts influence each other and interact bidirectionally with the key factors. The arrows represent the strength of this influence. While progress in BCI catalysts and their key performance indices will influence related disciplines, this influence will be weaker than its reverse. There will be a lot more research in related catalysts than BCI-specific catalysts. While new sensors developed for BCIs might inspire improved ExG sensors, 1 electronics, 1 This term is a contraction of electro-(anything)-gram. ExG sensors refer to any devices used to record people’s electrophysiological activity. The word “sensor” is used in its colloquial meaning throughout this chapter, not the stricter definition within electrical engineering.
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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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56 G. Pfurtscheller and C. Neuper B
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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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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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168 N. Birbaumer and P. Sauseng 8.
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Non Invasive BCIs for Neuroprosthes
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BCIs Based on Signals from Between
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258 K.J. Miller and J.G. Ojemann 26
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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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The First Commercial Brain-Computer
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Page 368 and 369: Toward Ubiquitous BCIs 363 facial m
Page 370 and 371: Toward Ubiquitous BCIs 365 The best
Page 372 and 373: Toward Ubiquitous BCIs 367 Across a
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Page 382 and 383: Toward Ubiquitous BCIs 377 Table 1
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Page 386 and 387: Toward Ubiquitous BCIs 381 may down
Page 388 and 389: Toward Ubiquitous BCIs 383 physicis
Page 390 and 391: Toward Ubiquitous BCIs 385 31. F.H.
Page 392 and 393: Toward Ubiquitous BCIs 387 67. G. S
Page 394 and 395: 390 Index 65, 157, 163, 217, 221-23
Page 396 and 397: 392 Index 208, 236, 243, 245, 260-2
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