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Non Invasive BCIs for Neuroprostheses Control of the Paralysed Hand 179 An example of such a system is given in Fig. 4c. In this case, the TH was computed over a time period of 240 s by performing two “idle” runs without any imagination. The band power was then extracted (15–19 Hz band) and the mean (x) and standard deviation (sd) calculated. Here, TH was set to TH = x +3· sd. To give the user enough time to changing his mental state, a refractory phase of 5 s was implemented. In this time period, the classification output could not trigger another grasp phase. The first two pictures of Fig. 7 presents two shots during drinking. 2.3.2 BCI-Training of Patient HK Using an Implanted Neuroprosthesis For practical reasons, training was performed over 3 days at the patient’s home. At first, the patient was asked to imagine different feet and left hand movements to determine which movements required the least concentration. After this prescreening, a cue-guided screening session was performed, where he was asked to imagine feet and left hand movements 160 times. Applying time-frequency analyses, the ERD/S maps were calculated. From these results, the most reactive frequency bands (14–16 and 18–22 Hz) were selected, and a classifier was set up. With this classifier, online feedback training was performed, which consisted of totally 25 training runs using the Basket paradigm [11]. The task in this Basket experiment was to move a ball, falling with constant speed (falling duration was 3 s) from the top of the screen, towards the indicated target (basket) to the left or to the rigth at the bottom of the screen. Four runs (consisting of 40 trials each) with the best accuracy were then used to adapt the frequency bands (resulting in 12–14 Hz and 18–22 Hz) and calculate a new classifier (offline accuracy was 71 %). This classifier was then used in an asynchronous paradigm for unguided training. Because of a significant ERD, left hand MI was used for switching. This decision was supported by the fact that, during the non-control state, foot activity was detected, so the classifier had a bias to the foot class. Therefore, the output of the classifier was compared with a threshold implemented in the class representing left hand movement imaginations (compare the brain switch scheme given in Fig. 2). Whenever the classifier output exceeded this threshold for a dwell time of 1 s, a switching signal was generated. Consecutively a refractory period of 3 s was implemented so that the movement control was stable and the probability of false positives was reduced. A 180-s run without any MI showed no switch-action. After the BCI-training paradigm, the classifier output of the BCI was coupled with the Freehand(R) system. In Fig. 5a the averaged LDA output during the evaluation experiment is presented. The predefined threshold in this case 0.7 was exceeded 1 s prior the real switching action (dwell time) at second 0. Fig. 5b shows the corresponding band power features [14]. 2.4 Interferences of Electrical Stimulation with the BCI A grasp neuroprosthesis produces short electrical impulses with up to 40 mA (= 40 V assuming 1 k� electrode-skin resistance) in a frequency range from 16 to 35 Hz. These strong stimulation pulses lead to interference in the μV-amplitude
180 G.R. Müller-Putz et al. Fig. 5 (a) Average LDA output for the classifier used during the grasp test (16 triggered grasp phases). (b) Corresponding averaged logarithmic band power features from electrode positions Cz and C4 during the grasp test (modified from [17]) EEG. In the case of the neuroprosthesis with surface electrodes, the EEG was recorded in a bipolar manner so that synchronized interference could effectively be suppressed. Since every stimulation channel had a bipolar design, the influence on EEG recordings was minimized. Compared to the use of surface electrodes there was a much higher degree of interferences apparent in the EEG using the Freehand system. This stimulation device consists of eight monopolar stimulation electrodes and only one common anode, which is the stimulator implanted in the chest. To avoid contamination by stimulation artifacts, spectral regions with these interferences were not included in the frequency bands chosen for band power calculation. When the analyzed frequency bands of the BCI and stimulation frequency of stimulation device overlap, the repetition rate of the stimulation pulses can be shifted towards a frequency outside of the reactive EEG bands. Techniques such as Laplacian EEG derivations or regression methods with additional artifact recording channels would inevitably lead to more electrodes and therefore be a severe limitation for the application of a EEG-based brain-switch in everyday life (Fig. 6). 2.5 Evaluation of the Overall Performance of the BCI Controlled Neuroprostheses For evaluation of the whole system performance in patient HK, a part of the internationally accepted grasp-and-release test [33] was carried out. The task was to repetitively move one paperweight from one place to another within a time interval of 3 min (see Fig. 7 picture 3 and 4). For the completion of one grasp sequence, 3 classified left hand MI were necessary. During this 3-min interval, the paperweight was moved 5 times with a sixth grasp sequence initiated. The mean duration of the 16 performed switches was 10.7 s ± 8.3 s [14].
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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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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 182 and 183: Non Invasive BCIs for Neuroprosthes
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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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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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274 J. Mellinger and G. Schalk Fig.
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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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