Brainâ€“Computer Interfaces - Index of

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250 K.J. Miller and J.G. Ojemann found a negative feedback between these processes. If the brain dynamics are trying to converge on given parameterization, and the adaptive algorithm is continuously changing the parameterization, there is the potential that no stable interface can be converged upon. Recent ECoG studies, however, have demonstrated that, for a simple ECoG BCI, stable control can be maintained for many consecutive days without any change in the feedback feature parameters [35]. 4 Feedback The feedback process is illustrated in Fig. 5. The potential, V(t), is measured from each electrode with respect to a ground, and amplified. The ground may come from the scalp, or from an internal ECoG electrode, but is dictated by the primary clinical system because of the necessity for a common ground between the two amplifiers. These potentials are then converted to an estimate of the power, in a specific range, at each point in time P(t) = f (V(t)). The function f(t) will first use a window in time to calculate a power spectrum – a longer window will produce a better estimate of the spectrum, a shorter window will allow for a higher effective bit-rate of the BCI, and the optimal window size will represent a tradeoff between the two (we typically use a quarter-second window). The power P(t) will be chosen from a confined frequency range from the spectrum. It is important that the window length be significantly longer than the time period of the lowest frequency, so that the lowest frequency used may be estimated with some accuracy. Papers that are submitted, and forget to do this, are rejected (really). The spectral estimation method used may be a Fourier transform technique, or may be a parameterized estimation technique, like the autoregressive model method. Parameterized techniques have the potential advantage of better spectral estimation using shorter time windows, but the drawback that it can be hard to know how many parameters to use (model order), and that different model orders can produce widely different results. We couple the power, Fig. 5 Schematic representation of the closed-loop brain–computer interface that we use
A Simple, Spectral-Change Based, Electrocorticographic Brain–Computer Interface 251 P(t), to the movement of a cursor in a given direction, according to the simple linear relation ˙y = g(P(t)−P0), where P0, is a power level somewhere between movement imagery and rest, determined from a screening task (as in Fig. 4). This difference is then coupled to the movement 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 views changes in the cursor trajectory, and the subject then modifies the nature of the imagery to cause the cursor to move toward their target choice. After 8–10 min, in the 1-D task, subjects will abstract away from the imagery and are able to imagine the movement of the cursor itself and produce appropriate spectral changes. The power in this identified feature, P0, is then linked to the velocity, ˙y, of a cursor on the computer screen, using the simple translation algorithm ˙y = g(P(t)−P0), where P0 is a power level somewhere between movement imagery and rest, and g is a velocity parameter. This difference is then coupled to the move- Fig. 6 Feature reassessment after feedback during a speech imagery task. (a) Two different feedback sites. An initial feedback site was chosen at site “1”, and, upon reassessment, a secondary feedback site was chosen at site “2”. (b) The feature map from a speech imagery screen identified a frontal site for control (labeled “1” in (a)), which was coupled to a cursor control task. The subject was not able to accurately control the cursor in the task, obtaining only 45% correct. (c) A feature map from this unsuccessful feedback run demonstrated that, while there was no significant difference at the feedback site, there was at a different site (labeled “2” in (A)). (d) The feature map following feature reassessment. The subject was able to rapidly attain 100% target accuracy with the new feature, and the most significant change was in the reassessed electrode
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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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Page 230 and 231: BCIs Based on Signals from Between
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The First Commercial Brain-Computer
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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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