Brainâ€“Computer Interfaces - Index of

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162 N. Birbaumer and P. Sauseng Fig. 3 On the left side of the figure, (a) the performance of each patient is depicted demonstrating significant learning in most patients except one. Within 20 sessions of training (1–2 h each), most patients were able to open and close their completely paralyzed hand with the help of the orthosis and their brain activity in more than 70–80% of the trials. Column; (b) displays a flat map of the spectral amplitude differences across the MEG array between both target conditions (increase SMR rhythm or decrease SMR rhythm). The sensor locations used to produce feedback and control of the orthosis are highlighted by green filled circles. Column (c) ofFig.3 displays a statistical map (r-square) of the correlation of the SMR rhythm amplitude across the MEG array with target location. Column (d) displays MR scans obtained for each subject. The red circles highlight the location of each patient’s lesion
Brain–Computer Interface in Neurorehabilitation 163 if simultaneous activation of both hemispheres should be allowed for the activation of the contralesional hand. Whether cortical reorganization after extended BCI training will allow the reinstatement of peripheral control remains an open question. By-passing the lesion by re-growth of new or non-used axonal connections in the primary motor output path seems highly unlikely. However, a contribution of fibres from the contralesional hemisphere reaching the contralesional hand to activate the paralysed hand is theoretically possible. In those cases, a generalisation of training from a peripheral orthosis or any other rehabilitative device to the real world condition seems to be possible. In most cases, however, patients will depend on the activation of electrical stimulation of the peripheral muscles or an orthosis as provided in the study of Buch et al. Some patients will also profit from an invasive approach using epicortical electrodes or microelectrodes implanted in the ipsilesional intact motor cortex, stimulating peripheral nerves or peripheral muscles with voluntary generated brain activity. A new study on healthy subjects using magnetoencephalography from our laboratory (see [35]) has shown that with a non-invasive device such as the MEG with a better spatial resolution than the EEG directional movements of the hand can be classified from magnetic fields online from one single sensor over the motor cortex. These studies demonstrate the potential of non-invasive recordings but do not exclude invasive approaches for a subgroup of patients non-responding to the non-invasive devices. Also, for real life use, invasive internalised BCI systems seem to function better because the range of brain activity usable for peripheral devices is much larger and artefacts from movements do not affect implanted electrodes. The discussion whether invasive or non-invasive BCI devices should be used is superfluous: only a few cases select the invasive approaches, and it is an empirical and not a question of opinion which of the two methods under which conditions provide the better results. 5 The “Emotional” BCI All reported BCI systems use cortical signals to drive an external device or a computer. EEG, MEG and ECoG as well as near infrared spectroscopy do not allow the use of subcortical brain activity. Many neurological and psychiatric and psychological disorders, however, are caused by pathophysiological changes in subcortical nuclei or by disturbed connectivity and connectivity dynamics between cortical and subcortical and subcortical-cortical areas of the brain. Therefore, particularly for emotional disorders, caused by subcortical alterations brain–computer interfaces using limbic or paralimbic areas are highly desirable. The only non-invasive approach possible for operant conditioning of subcortical brain activity in humans is functional magnetic resonance imaging (fMRI). The recording of blood-flow and conditioning of blood-flow with positron emission tomography (PET) does not constitute a viable alternative because the time delay between the neuronal response, its neurochemical consequences and the external reward is variable and too long to be used for successive learning. That is not the case in functional magnetic resonance imaging, where special gradients and echo-planar imaging allows on-line feedback
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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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56 G. Pfurtscheller and C. Neuper B
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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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Page 126 and 127: Detecting Mental States by Machine
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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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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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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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350 A. Schlögl et al. Table 3 Aver
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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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