Brainâ€“Computer Interfaces - Index of

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156 N. Birbaumer and P. Sauseng C A/DU –50 0 50 100 B A1 G Fp1 Fp2 F7 F3 FZ F4 F8 T3 C3 A P3 PZ T5 P4 T6 O1 OZ O2 P300 event-related potential PZ –100 0 150 100 200 300 400 ms 500 EEG-headbox 1 2 3 4 5 6 patient CZ C4 T4 standards target row/column target letter (P) amplitude ( μV) A2 ABCDEF GH I JK L MNO PQR STUV WX Y5 Z 17 2 39 4 6 8 - F –10 0 10 20 EEG-amplifier slow cortical potentials 0 0,5 1,0 1,5 2,0 time (s) amplitude ( μV) D E –7 μv +7 μv 4 3 2 1 0 0 EEG frequency pattern - motor imagery ERD (%) alpha band 50 25 C3 C4 0 –25 PC notebook for feedback –50 0 1 2 3 4 5 6 7 8s 0 1 2 3 4 5 6 7 8s left motor imagery right motor imagery top target bottom target - rhythm 5 10 15 frequency 20 25 30 Fig. 1 Basic principle of a typical EEG BCI. (a)and(b) show the setup of a BCI. (c–f) Examples of EEG signals used for BCI 1 sec 10 μV
Brain–Computer Interface in Neurorehabilitation 157 Figure 1 shows the most frequently used EEG BCI. More recently, a BCI system based on magnetoencephalography was tested [8] and applied to stroke rehabilitation. On an experimental basis near-infrared spectroscopy (NIRS) and functional magnetic resonance-BCIs were introduced by our group [33, 10]. Of particular interest is the use of signals ranging in spatial resolution between single spikes and local field potentials (LFP) and the EEG, namely the electrocorticogramm (ECoG). Electrical signals recorded subdurally or epidurally provide a better spatial resolution with the same exquisite time resolution as EEG but avoids the smearing and filtering of the EEG signals. In addition, it allows recording and training of high frequency components particularly in the gamma range (from 30 to 100 Hz). First attempts to classify movement directions from the electrocorticogramm were particularly successful (see [2, 22, 35, 38]; see also chapters “BCIs Based on Signals from Between the Brain and Skull” and “A Simple, Spectral-change Based, Electrocorticographic Brain–Computer Interface” in this book). At present, brain– computer interface research in neurorehabilitation is dominated by interesting basic science approaches holding great promises for human applications but very modest clinical use (see [7]). As in the tradition of clinical applications of neurofeedback, the field is lacking large-scale clinical studies with appropriate controls proving the superior efficiency of BCI systems over classical rehabilitation attempts. 2 Basic Research Animal research stimulated to a large extent the interest and enthusiasm in BCI research. Nicolelis [26, 27] summarized these attempts: using more than 100 electrodes in the motor cortex or in the parietal cortex projecting into the motor system monkeys were trained to move a prosthesis mounted outside their body or even in another laboratory using spike sequences; monkeys were trained over long time periods to move prostheses with spike frequencies and were rewarded when the peripheral orthoses performed an aiming or reaching movement usually grapping a food reward. Even more impressive were studies from the laboratory of Eberhard Fetz [15] with operant conditioning of spike sequences. Monkeys were able to produce many different types of single cell activity, rhythmic and non-rhythmic, and used the trained spike sequences for manipulating external machinery. Of particular interest was the demonstration of a neurochip in which the monkey learned to operate an electronic implant with action potentials recorded on one electrode to trigger electrical stimuli delivered to another location on the cortex. After several days of training, the output produced from the recording site shifted to resemble the output from the corresponding stimulation site, consistent with a potentiation of synaptic connections between the artificially synchronized neurons. The monkeys themselves produced long-term motor cortex plasticity by activating the electronic neuronal implant. These types of Hebbian implants may turn out to be of fundamental importance for neurorehabilitation after brain damage using the brain–computer interface to permanently change cortical plasticity in areas damaged or suppressed by pathophysiological activity.
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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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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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BCIs Based on Signals from Between
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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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264 J. Mellinger and G. Schalk Fig.
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266 J. Mellinger and G. Schalk Filt
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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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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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332 A. Schlögl et al. Fig. 1 Schem
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338 A. Schlögl et al. Fig. 2 State
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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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