Brainâ€“Computer Interfaces - Index of

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Non Invasive BCIs for Neuroprostheses Control of the Paralysed Hand 175 Fig. 2 General idea of a brain-switch. (a) Features of different classes (e.g., left hand, right hand or feet MI) are usually separated by an optimal decision border. Introducing an additional threshold a switch function can be designed. (b) LDA output of two types of MI during no MI and MI does not really change from a period without MI to a period with MI. However, the other class (class 2) does. A significant change can be seen. It is assumed that the first case (class 1) is a very general case. This means that the classifier would also select this class when no MI is performed. Therefore, this describes the non-control state. Introducing an additional threshold into the class of the other MI pattern (class 2), a switch function can be designed (control state). A control signal is only triggered when the MI is recognized clearly enough to exceed the threshold. For the BCI application (see Fig. 3), the grasp is divided into distinct phases (Fig. 3c, d), e.g., hand open, hand close, hand open, hand relax (stimulation off). Whenever a trigger signal is induced, the users’ hand is stimulated so that the current phase is subsequently switched to the next grasp phase. If the end of the grasp sequence is reached, a new cycle is started. 2.1 Patients The first patient enrolled in the study, TS, is a 32 year old man who became tetraplegic because of a traumatic spinal cord injury in April 1998. He has a complete (ASIA A, described by the American Spinal Cord Injury Association, [30]) motor and sensory paralysis at the level of the 5th cervical spinal vertebra. Volitional
176 G.R. Müller-Putz et al. Fig. 3 Application of a BCI as a control system for neuroprostheses based on surface (a) and implanted (b) electrodes, respectively. (c) Grasp pattern for palmar grasp. (d) Grasp pattern for lateral grasp muscle function is preserved in both shoulders and in his left biceps muscle for active elbow flexion, as well as very weak extension. He has no active hand and finger function. As a preparation for the neuroprosthesis he performed a stimulation training program using surface electrodes for both shoulders and the left arm/hand (lasting about 10 months) with increasing stimulation frequency and stimulation time per day until he achieved a strong and fatigue resistant contraction of the paralyzed muscles of the left (non-dominant) forearm and hand. The stimulation device used for this training was then configured per software as a grasp neuroprosthesis by implementing the stimulation patterns of three distinct grasp phases (Microstim, Krauth & Timmermann, Hamburg, Germany). The second patient, a 42 year old man called HK, has a neurological status like TS, with a complete (ASIA A) motor and sensory paralysis at the 5th cervical spinal vertebra because of a car accident in December 1998. His volitional muscle activation is restricted to both shoulders and bilateral elbow flexion with no active movements of his hands or fingers. He underwent a muscle conditioning program
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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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Page 150 and 151: 138 Y. Wang et al. which has been e
Page 152 and 153: 140 Y. Wang et al. After many studi
Page 154 and 155: 142 Y. Wang et al. Left Hand Right
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Page 162 and 163: 150 Y. Wang et al. be summarized as
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Page 182 and 183: Non Invasive BCIs for Neuroprosthes
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Page 230 and 231: BCIs Based on Signals from Between
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BCIs Based on Signals from Between
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242 K.J. Miller and J.G. Ojemann 2
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244 K.J. Miller and J.G. Ojemann ha
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246 K.J. Miller and J.G. Ojemann Fi
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248 K.J. Miller and J.G. Ojemann Fi
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250 K.J. Miller and J.G. Ojemann fo
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252 K.J. Miller and J.G. Ojemann me
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254 K.J. Miller and J.G. Ojemann In
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256 K.J. Miller and J.G. Ojemann ar
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258 K.J. Miller and J.G. Ojemann 26
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260 J. Mellinger and G. Schalk mapp
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262 J. Mellinger and G. Schalk 2 BC
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264 J. Mellinger and G. Schalk Fig.
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266 J. Mellinger and G. Schalk Filt
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268 J. Mellinger and G. Schalk proc
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270 J. Mellinger and G. Schalk exis
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272 J. Mellinger and G. Schalk reco
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274 J. Mellinger and G. Schalk Fig.
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276 J. Mellinger and G. Schalk numb
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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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