Brainâ€“Computer Interfaces - Index of

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260 J. Mellinger and G. Schalk mapping of cortical function using ECoG [3, 20, 24, 26]; the optimization of BCI signal processing routines [5, 23, 37]; the demonstration that two-dimensional hand movement trajectories can be decoded from ECoG signals [25]; and evaluation of steady-state visual evoked potentials (SSVEP) for the BCI purpose [1]. A number of these studies were done across different laboratories, thereby taking advantage of the common exchange of data and experimental paradigms supported by BCI2000. In summary, BCI2000 is a critical tool for challenging one of the greatest problems with BCIs: the need for considerable time and energy to set up and configure a BCI system. Much BCI research still involves laboratory demonstrations of highly specialized and mutually incompatible BCI systems, or only isolated components of a BCI such as a new signal processing mechanism. By providing a straightforward, easy, usable platform with strong support, BCI2000 has strongly catalyzed the emerging transition to flexible, practical, and clinically relevant BCI systems. This transition is essential to making BCIs practical to severely disabled and other users. As indicated by the above descriptions of its capacities, its utility for many different aspects of BCI research, the success of its wide dissemination, and its growing prominence in the scientific literature, BCI2000 is fast becoming, or perhaps has already become, a standard software platform for BCI research and development. BCI2000 is available free of charge with full documentation and complete source code at http://www.bci2000.org for research and educational purposes. See Sect. 5 for further information about dissemination and availability. In this book chapter, we will introduce the reader to the BCI2000 system and give an overview of its design, capabilities, and use. We will also present a number of scenarios that are typical of applications in BCI research, and discuss how BCI2000 may be used to implement them. While all these scenarios are different, we show how each of them benefits from the use of the system. BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments by substantially reducing labor and cost. It does this mainly by providing a number of capable BCI and stimulus presentation paradigms that can either be configured by the investigator or adapted by a software engineer. Because BCI2000 is based on a system model that can describe any BCI system, because its underlying framework is highly generic, and because BCI2000 does not require third-party components, its use is most beneficial in large collaborative research programs with many concurrent and different experiments in different locations. BCI2000 provides these benefits through the following features: 1.1 Proven Components BCI2000 is provided with fully documented components that have proven to work robustly in many different BCI and other experiments. By its modular design, BCI2000 tries to avoid redundancies in code, and re-uses modules and code in multiple contexts rather than reduplicating it. One of the advantages of this approach is
Using BCI2000 in BCI Research 261 that it maximizes the effect of errors that may exist in any single component, greatly increasing the likelihood to detect such errors early. 1.2 Documentation We provide hundreds of pages of documentation that encompass user, technical, and programming references that cover all major components. In addition, we include user tutorials that provide an introduction to general BCI operation using sensorimotor rhythms (i.e., mu/beta rhythms) and P300 evoked potentials. This documentation is maintained as a wiki information system on doc.bci2000.org, and is deployed with BCI2000 as HTML help pages. Additionally, these help pages are linked to the BCI2000 user interface, providing an instant reference. 1.3 Adaptability Because of its flexible and modular design, BCI2000 allows combination of existing software components with relatively little effort. This ranges from simple re-combination of modules (e.g., using the same signal processing algorithm with different feedback paradigms), to modification of the kind and order of signal processing components, and to modification of existing components or creation of new components. 1.4 Access BCI2000 comes with full source code. Thus, the user may adapt it to individual needs. In most cases, modifying an existing system is easier than creating a new one from scratch. 1.5 Deployment BCI2000 facilitates deployment to, and collaboration amongst, multiple sites. It is very easy to install because it comes as a simple directory tree, and does not require any specific installation process other than copying this directory tree. Installation and operation is even possible with limited user rights. Aside from hardware drivers necessary for the data acquisition device, BCI2000 does not depend on third-party components. Collaboration is facilitated by generic data and parameter files. Thus, experimental paradigms can be designed in one location, tested in another, and the resulting data files can be easily interpreted. In the following section, we will first give an overview of BCI2000’s basic design and components, and then move on to discuss research scenarios that may profit from the use of BCI2000.
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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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Page 230 and 231: BCIs Based on Signals from Between
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Page 272 and 273: 264 J. Mellinger and G. Schalk Fig.
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312 Y. Li et al. components is larg
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314 Y. Li et al. P300-based, and ER
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316 Y. Li et al. 3.3.2 Autoregressi
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318 Y. Li et al. selection as follo
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320 Y. Li et al. 5.1.2 Support Vect
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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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328 Y. Li et al. 6. M. Cheng, X. Ga
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330 Y. Li et al. 46. K. Crammer and
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332 A. Schlögl et al. Fig. 1 Schem
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334 A. Schlögl et al. For the rect
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336 A. Schlögl et al. whereby T in
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338 A. Schlögl et al. Fig. 2 State
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340 A. Schlögl et al. yk = a1 · y
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342 A. Schlögl et al. d{i}(x) = ((
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344 A. Schlögl et al. Accordingly,
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346 A. Schlögl et al. not exceed a
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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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