Brainâ€“Computer Interfaces - Index of

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BCIs Based on Signals from Between the Brain and Skull 227 ability to add additional electrodes or to influence the locations at which the clinically placed electrodes are located. Thus, ECoG-based BCI studies usually include large numbers of recordings from electrodes placed over areas that are suboptimal for the task being studied. Additionally, not all eligible subjects will agree to participate in research. Some subjects are excited by the opportunity to “give back” to medical research by participating in research that will hopefully help others as they are themselves being helped. However, other subjects are tired, distracted, in pain (they have just undergone brain surgery after all), apprehensive, or simply sick of medical procedures and thankful to have something to which they can say “no.” This further limits the number of subjects available for ECoG research. Further, working with a patient population in a clinical environment severely limits time with the subjects. Patient care activities take priority over research and medical personnel frequently exercise the right to interact with the patient, sometimes in the middle of an experiment. Further, the patient’s time is scheduled for clinically important tasks such as taking medication, monitoring vital signs, cortical stimulation mapping, and procedures such as CT scans and personal activities such as family visits. Other factors that may affect the brain activity are also out of the researcher’s control. Subjects may be on a variety of seizure medications, or they may have reduced their normal seizure medications in an effort to precipitate a seizure so that its location of origin can be identified. Other measures, such as sleep deprivation or extra exercise, may also be ongoing in an effort to precipitate a seizure. Finally, a patient may have had a seizure recently, which can dramatically affect concentration and fatigue in a manner unique to that individual. Of course, a subject may also have a seizure during an experiment, which could be only a minor interruption, or could bring the experimental session to an end, depending on the manifestation of the seizure. These issues highlight the generally understood, though perhaps seldom stated, fact that the brains of subjects who have ECoG electrodes placed for epilepsy surgery (or any other clinical condition) are not in fact “normal” examples of the human brain. While the brain of a person who needs a BCI may also not be “normal” (depending on the reason the BCI is needed), there is no evidence that the subjects who currently provide access to ECoG are good models for people who need a BCI; they are simply the only models available. In addition to practical issues of experimental control, there are practical technical challenges. Since the ECoG electrodes have been placed for clinical purposes, monitoring using these electrodes cannot be interrupted during the experiment. While digital recording of brain activity is now standard during epilepsy monitoring, the ability to provide custom analysis and BCI operation with a clinical recording setup is not. So, any experiment that involves more than passively recording subjects’ brain activity during known actions may require a separate machine to record and process the ECoG for BCI operation. Using this machine to temporarily replace the clinical recording machine for the duration of the experimental setup would interrupt standard patient care and would therefore require close involvement of all clinical epilepsy monitoring personnel and an unusually flexible patient care regime, although it has been reported [5]. A more common recording setup (see Fig. 3)
228 J.E. Huggins Fig. 3 Diagram of equipment setup for a BCI experiment during clinical monitoring therefore includes splitting the analog signals from the ECoG electrodes prior to digitization so that they can be sent both to the clinical recording equipment and to the BCI. This setup must be carefully tested according to hospital regulations for electrical safety. While signal splitting can increase the electrical noise apparent in the ECoG signals, it has been successfully used by several researchers [3, 17]. The sheer quantity of electrical devices that are used in the hospital presents an additional technical challenge that the high quality of clinical recording equipment can only partly overcome. Sources of electrical noise in a hospital room can range from autocycling pressure cuffs on a patient’s legs that are intended to reduce post-operative complications, to medical equipment being used in the next room (or occasionally by the patient sharing a semi-private room), to other electrical devices such as the hospital bed itself. Vulnerability to electrical noise may be increased by the location of the electrodes used as the ground and recording reference. These important electrodes may be ECoG electrodes, bone screw electrodes or scalp electrodes. When a scalp electrode is used as the reference, vulnerability to widespread electrical noise can easily result. 5 Successful ECoG-Based BCI Research The use of ECoG for BCI research has advantages of signal quality, spatial resolution, stability, and (eventually) ease of BCI setup. ECoG can be obtained from large areas of the cortex, and ECoG shows event-related signal changes in both
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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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Non Invasive BCIs for Neuroprosthes
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278 J. Mellinger and G. Schalk 5. A
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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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