Brainâ€“Computer Interfaces - Index of

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BCIs Based on Signals from Between the Brain and Skull 235 6.2.5 Tuebingen, Germany Birbaumer’s well-established EEG-based BCI work has led to an initial ECoGbased BCI study [43]. This work used off-line analysis of ECoG and compared it to off-line analysis of EEG recorded from different subjects under a slightly different paradigm. The algorithm performance on ECoG was comparable to that found for EEG results, although the maximum results for EEG were better than those for ECoG. However, the number of trials, sampling rates, and applied filtering (among other parameters) were different, making conclusions difficult. Of greater interest is a single ECoG recording experiment done with a completely paralyzed subject as part of a larger study comparing BCI detection algorithm function on brain activity from healthy subjects and subjects with complete paralysis [44]. The subject gave informed consent for surgical implantation of ECoG electrodes by using voluntarily control of mouth pH to communicate [44, 45]. However, the BCI detection algorithms in this study did not produce results greater than chance for any of the subjects who were completely paralyzed (4 with EEG electrodes and 1 with ECoG). See the Chapters “Brain–Computer Interface in Neurorehabilitation” and “Brain– Computer Interfaces for Communication and Control in Locked-in Patients” in this volume for further discussion of this issue. 6.2.6 University Hospital of Utrecht Ramsey et al. [46] included ECoG analysis as a minor component (1 subject) of their fMRI study of working memory as a potential BCI input. They looked at the ECoG near the onset of a working memory load and reported the increased activity that occurred in averaged ECoG. Although they were able to show agreement between the fMRI and ECoG modalities, they did not do actual BCI detection experiments either online or offline. Further, the necessary use of working memory in any task for which BCI operation of technology was desired makes this signal source appear to be only practical for detecting concentration or perhaps intent to perform a task. As such, working memory detection could be used to gate the output of another BCI detection method, blocking control signals from the other detection method unless working memory was also engaged. While this might improve BCI function during no-control periods, by requiring concentration during BCI operation, it may also limit BCI use for recreational tasks such as low intensity channel surfing. Further, it could falsely activate BCI function during composition tasks when the user was not yet ready to start BCI typing. 6.2.7 The University of Michigan – Ann Arbor (Kipke) In a recent study using microelectrodes in rats performing a motor learning task in Kipke’s Neural Engineering Laboratory, ECoG from bone-screw electrodes was analyzed in addition to neural spike activity and local field potentials from microelectrodes [2]. This work found ECoG signals that occurred only during the learning period. Comparison of ECoG to local field potentials recorded from microelectrodes
236 J.E. Huggins at different cortical depths revealed that ECoG includes brain activity evident at different cortical levels, including some frequencies that were only apparent on the deepest cortical levels. 6.2.8 University of Florida – Gainesville A 2008 report applied principles developed using microelectrode recordings to ECoG recorded at 12 kHz during reaching and pointing tasks by 2 subjects [5]. On average, the correlation coefficients between actual movement trajectory and predicted trajectory were between 0.33 and 0.48 for different frequency bands, position coordinates and subjects. Different frequency bands were found to better correlate with different parts of the movement trajectory with higher frequencies (300 Hz– 6 kHz) showing the best correlation with the y-coordinate. Specific instances of high correlation (above 0.90) indicate that specific tasks are better represented than others. This exploration of ECoG frequencies far outside those used for most studies has revealed a hitherto unknown information source and presents exciting opportunities for future work. 6.2.9 Albert-Ludwigs-University, Freiburg, Germany Another 2008 report also applies BCI methods developed using microelectrode recordings to ECoG during arm movements [6]. Using low frequency components (below about 10 Hz) from 4-electrode subsets of the available electrodes, an average correlation coefficient of 0.43 was found for subjects with ECoG from electrodes over motor cortex containing minimal epileptic artifact. Higher frequency components were also found to correlate with movement trajectory, but their inclusion did not improve the overall prediction accuracy. The authors point out many areas for future work, including recording even broader frequency bands, the use of denser grids of smaller electrode, and provision of real-time feedback to the subjects. 7 Discussion ECoG is now increasingly included in BCI research. Many BCI research groups pursuing other approaches are starting to incorporate or explore ECoG. The degree to which ECoG-based BCI research becomes a permanent part of the work of these research groups has yet to be seen. Of course, ECoG research is also ongoing for both basic research on brain function and for applications such as epilepsy surgery. A recent paper on real-time functional mapping of brain areas for epilepsy surgery [47] included both background and discussion on potential BCI applications of their work, perhaps indicating a generally increased awareness and acceptance of BCI applications. While ECoG provides advantages over EEG and microelectrode signal sources, the knowledge developed through the pursuit of ECoG-based BCIs should be applicable to other signal types. Signal processing methods that apply to ECoG may also be useful for EEG. Further, methods developed and tested using ECoG may
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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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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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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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