Brainâ€“Computer Interfaces - Index of

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252 K.J. Miller and J.G. Ojemann ment of a cursor, with speed determined by the parameter g, which can be adjusted in the beginning of the task to be comfortable to the subject. The subject is instructed to adjust the position of the cursor so that it hits a target (Fig. 5). Modification of the imagery behavior is allowed, even encouraged, to maximize target accuracy. Fig. 7 A single case demonstration of the entire BCI process
A Simple, Spectral-Change Based, Electrocorticographic Brain–Computer Interface 253 Interestingly, even with the most robust signal features, once the behavior is linked to a different task (i.e., the subject is now aiming for a target not just performing the given movement), the electrocorticographic signal may change. We have used the concept of “re-screening” (illustrated in Fig. 6) to make use of the fact that a given signal at a given electrode may be subtly or even dramatically different when the task condition is changed to include a BCI component. For applications in patients with certain neurologic impairments that impair movement, overt motor activity will, of course, not be accessible for a BCI device. Therefore, attempting to drive BCI with other features is of particular interest. The methods discussed here employ imagined movement and imagined speech to generate screening features. The areas of cortex engaged by, for example, imagined hand movement are remarkably similar to those involved in overt hand movement, though of much weaker strength (Fig. 7a). Because of the weaker signal, initial control can be more difficult with an imagined task, however, the presence of cursor feedback reliably produces an enhancement of the ECoG signal resulting in improved performance. This remarkable ability to enhance the signal has been reported in different imagined motor tasks and silent speech [36]. In most circumstances, accuracy increases over time, usually within a few trials (Fig. 7b,see[8, 10]). BCI2000 allows for a recursive tuning of the weights of a given feature so that the program learns, based on the signal of correct and incorrect trials, what the ideal translation between feature and cursor position should be. However, subjects show a robust learning on their own – the brain is able to learn, as with any new motor task (riding a bike, etc) to subconsciously modify activity based on feedback. Taken to an extreme, this concept has allowed BCI to occur even when the behavior of the cortex underlying the chosen electrode is less well defined [12]. Though originally the cursor control is explicitly linked to the behavior, the subject is free to explore mental states that achieve better cursor control. Anecdotally, successful control often is associated with a wide range of experiential strategies. Some control evolves to being achieved with a throat sensation or, in the most striking example of behavioral plasticity, the subject simply imagines the cursor to move in the preferred direction, no longer using the originally prescribed behavior as an intermediate, as happened with the subject described in the case study at the end of this chapter. 5Learning In this particular paradigm, stability of signal is hard to determine given the primary purpose of the implants is clinical and the implant is removed once the seizure focus is determined. However, we have had occasion to assess the stability over multiple days in a 5 day, repeated testing region. Since the recording devices are on the brain, not fixed to it, one could imagine day-to-day variations in the signal. In fact, the use of ECoG appears to rely upon a population signal that is not overly sensitive to daily fluctuations [35] and the same features can be reused with minimal re-training required to achieve accurate cursor control.
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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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56 G. Pfurtscheller and C. Neuper B
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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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Page 230 and 231: BCIs Based on Signals from Between
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The First Commercial Brain-Computer
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306 Y. Li et al. Fig. 1 Basic desig
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308 Y. Li et al. Fig. 2 A linear tr
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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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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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