Brainâ€“Computer Interfaces - Index of

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Toward Ubiquitous BCIs 365 The best opportunity for increasing effective throughput is through these clever interface features [3, 80]. This is relatively virgin territory within BCI research. While improving raw ITR is still important, this task has consumed the bulk of BCI research. Many articles about BCIs present novel signal processing tools or combinations that are applied offline to existing data, often from a different lab, and produce a marginal performance improvement in some subjects. ITR could be improved more by increasing the number of mental tasks used either simultaneously or sequentially, such as with a hybrid BCI, but the main focus should be on interfaces that can do more with a specific ITR. Hardware innovations could also improve the effective throughput. For example, improved amplifiers or sensors that provide a cleaner picture of brain activity could result in fewer errors. Hardware improvements are worthwhile, but the best overall opportunity for increasing effective throughput remains software with clever interface features that can allow people to convey more useful information with fewer bits per minute. 2.4 Utility What can you do with a BCI? Right now, most BCIs control simple monitor-based applications, such as a speller or cursor with a basic interface and graphics. More advanced monitor and VR applications are emerging, such as BCIs to control Smart Homes, immersive games, or virtual environments [20, 41, 55, 64, 72, 74] (see also chapters “The Graz Brain–Computer Interface” and “The First Commercial Brain– Computer Interface Environment” in this book). So too are BCIs to control devices such as orthoses, prostheses, robotic arms, mobile robots, or advanced rehabilitation robotic systems such as a combined wheelchair and robotic arm [26, 30, 60]. Hence, both software and hardware advances are being made, but BCIs still control a lot less than mainstream interfaces. BCI utility must and will increase well before BCIs become ubiquitous. BCI utility reflects how useful a user considers a BCI. Can the BCI meet a potential user’s needs or desires? If not, then s/he will probably not use it, even if it is extremely inexpensive, easy to use, portable, etc. BCI utility is subjective on an individual level, but can be measured more objectively over larger groups. A BCI will not seem useful to a user who cannot get it to work. Support availability is a major issue today. Right now, an expert is typically required to identify the right components, put them together, customize different parameters, and fix problems (see chapter “Brain–Computer Interfaces for Communication and Control in Locked-in Patients”). Efforts to reduce dependence on outside help are essential. A BCI tech support infrastructure must develop to support new and sometimes frustrated users. Modern BCIs are relatively inflexible; one BCI can spell or move a cursor or browse the internet or control a robot arm, but not switch between these applications. Flexibility could be improved with a Universal Application Interface (UAI)
366 B.Z. Allison that could let users easily switch between BCI applications using any of the major BCI approaches (P300, SSVEP, ERD, 2 see chapters “Brain Signals for Brain– Computer Interfaces” and “Dynamics of Sensorimotor Oscillations in a Motor Task” for details). Users might spell, use communications and entertainment tools, operate wheelchairs, and control Smart Home devices through the same interface. The UAI should adapt to each user’s needs, abilities, desires, environment, and brain activity [3]. Ongoing BCI2000 and OpenVibe developments are increasing the number of available input and output devices. When BCIs attain the same flexibility as keyboards or mice, users will be able to think anything you could type or click. This milestone will result partly from better integration with existing software. Panel B of Fig. 2 below shows an example of how a BCI could be modified to increase its flexibility. The display is identical to the clever Hex-O-Spell system used by the Berlin BCI group (chapter “Detecting Mental States by Machine Learning Techniques: The Berlin Brain–Computer Interface”), except that two arrows have been added to the top of the display. These arrows might rotate the Hex-O-Spell display forward or back to reveal additional displays with added functionality, such as Hex-O-Play, Hex-O-Home, or Hex-O-Move. The idea of grouping commands within hexes to allow two levels of selection might be extended within these new applications. For example, in a Hex-O-Home system, the hexes might each represent a different Smart Home device, such as lights, windows and blinds, cameras, or heaters, and the commands that could be issued to each device would be within each hex. A Hex-O-Select system in which hexes represent different applications such as Smart Home, Internet, Music, or Game might serve as a type of UAI. 3 As BCI research increases, and more diverse people try out BCIs, reliability is becoming an increasingly obvious problem. Ideally, a BCI system should work for any subject, any time, in any environment. No such BCI has been developed. A B C 13 14 15 16 17 18 Fig. 2 Three possible hybrid BCI systems. Panel (a): A P300/SSVEP BCI based on the Donchin matrix. Panel (b): A hybrid ERD/P300 or SSVEP BCI based on the Berlin Hex-O-Spell. Panel (c): An original system that could use all three BCI approaches 2Some groups use the term mu or SMR instead of ERD 3Our team with the BrainAble research project at www.brainable.org is working toward implementing Hex-O-Select. UP
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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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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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94 G. Pfurtscheller et al. 10. D. F
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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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310 Y. Li et al. The large Laplacia
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312 Y. Li et al. components is larg
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Page 362 and 363: Toward Ubiquitous BCIs Brendan Z. A
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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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