Brainâ€“Computer Interfaces - Index of

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BCIs Based on Signals from Between the Brain and Skull 229 the time domain and the frequency domain (see Fig. 4). However, special attention must be paid to the technical challenges of working in an electrically noisy hospital environment within the limited time subjects have available during the constant demands of treatment in a working medical facility. An ECoG-based BCI project should pay special attention to these concerns from the earliest planning stages. Equipment selection and interfaces with clinical equipment should be designed for ease of setup, minimal disruption of clinical routine, and robustness to and identification of electrical contamination on either the ECoG or reference electrode signals. Feedback paradigms and experimental instructions should be quick to explain and easy to understand. Experiments should be designed to be compatible with the limited time available, the unpredictable nature of subject recruitment, and the limited control over basic experimental parameters such as electrode location. While the technical challenges of ECoG recording can usually be conquered by engineering solutions, the challenges of scheduling, access, and interruptions will be better addressed through the active involvement of the medical personnel on the research team. Ideally, these research team members should be excited about the research and have a specific set of research questions of particular interest to them. Since the medical team members may be the logical ones to recruit subjects into the experiment, and act as a liaison between the BCI research team and the clinical personnel, they can have a make-or-break effect on the total project success. Working as a team, a multidisciplinary research group can successfully utilize the opportunity provided by clinical implantation of ECoG electrodes for a variety of medically necessary treatment regimes to conduct strong BCI research. Fig. 4 Event-related potentials and ERD/ERS maps of movement-related ECoG for a subject performing right middle finger extension. (a) Electrode locations. (b) Averaged ECoG for the black electrodes in (a). (c) ERD/ERS for the black electrodes
230 J.E. Huggins 6 Past and Present ECoG Research for BCI Although human ECoG has been used for scientific study of brain function and neurological disorders since at least the 1960s [e.g. [18]], its use for BCI research was almost nonexistent prior to the 1990s. The earliest BCI researchers either worked with animals to develop tools for recording the activity of individual neurons (e.g., [16, 19]) or worked with human EEG (e.g.[20, 21]). 6.1 ECoG Animal Research The first ECoG studies for BCI development were reported in 1972 by Brindley and Craggs [22, 23] who studied the signals from epidural [22] electrode arrays implanted over the baboon motor cortex [22, 23]. ECoG was bandpass filtered to 80–250 Hz and the mean square of the signal calculated to produce a characteristic signal shape preceding specific movements that was localized over arm or leg motor cortex. A simple threshold detector was applied to this feature, generating prediction accuracy as high as 90%. 6.2 Human ECoG Studies 6.2.1 Smith-Kettlewell Eye Research Institute The first human BCI work using ECoG was reported in 1989 by Sutter [8, 24]. Epidurally implanted electrodes over visual cortex provided the input to a corticallybased eyegaze tracking system. The system was developed and tested using visual evoked potentials (VEP) in EEG. However, implanted electrodes were used when a subject with ALS had difficulty with artifact due to uncontrolled muscle activity and signal variations due to variable electrode placement by caregivers. Subjects viewed a grid of 64 flickering blocks. The block at the center of the subject’s field of vision could be identified because the lag between stimulus and VEP onset was known. While this interface did not claim to be operable without physical movement, and is therefore not a “true” BCI, it did demonstrate the use of large intracranial electrodes as the signal source for an assistive technology interface. 6.2.2 The University of Michigan – Ann Arbor (Levine and Huggins) Levine and Huggins’ UM-DBI project was the first targeted exploration of human ECoG for BCI development [3, 25, 26]. The UM-DBI project recorded from their first subject on the 27th July 1994. The UM-DBI project has focused on the detection of actions in a continuous segment of ECoG data during which subjects repeated actions at their own pace. ECoG related to actual movement has been used almost exclusively to allow good documentation of the time at which the subject chose to do the action.
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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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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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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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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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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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336 A. Schlögl et al. whereby T in
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338 A. Schlögl et al. Fig. 2 State
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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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