Brainâ€“Computer Interfaces - Index of

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Toward Ubiquitous BCIs 359 BCI Challenges Sensors Tasks Brain Effects Related Disciplines Electronics Signal Processing HCI Cognitive Neurosci. Communications Medicine Psychology Manufacturing Wearable Computing ExG Sensors Fig. 1 Key factors in BCI adoption and catalysts Key Factors Cost (financial, help, expertise, training, invasiveness, time, attention, fatigue) Throughput (alphabet, accuracy, speed, latency, effective throughput) Utility (support, flexibility, reliability, illiteracy) Integration (functional, distraction quotient, hybrid/combined BCIs, usability) Appearance (cosmesis, style, media, advertising) manufacturing, or wearable computing, improvements in these or other related disciplines will have a greater effect on BCI sensors. 2.1 BCI Catalysts Sensors refer to devices used to monitor brain activity. Most BCIs rely on noninvasive electrical measures of brain activity [44] – that is, brainwaves recorded from the surface of the scalp. In ten years, most BCIs will probably still use scalp recorded EEGs, but this view is far from universal. There has been great attention in the BCI literature to BCIs based on other brain monitoring technologies such as fMRI, MEG, or fNIR [84]. These approaches are overrated for BCI applications. None of these approaches has allowed an ITR comparable to EEG-based BCIs. fMRI and MEG require equipment that is not remotely portable and costs millions of dollars. fNIR is a bit more promising, since it is less expensive and more portable. Similarly, there has been more work describing invasive BCI systems recently [16, 31, 34, 38, 71] and corresponding enthusiasm for providing much greater benefits to patients (see Chapters “Intracortical BCIs: A Brief History of Neural Timing”, BCIs Based on Signals from Between the Brain and Skull”, and “A simple, Spectral–Change Based, Electrocorticographic Brain–Computer Interface” in this book). How can BCI sensors be improved? Modern laboratory EEG sensors have several problems. An expert must rub the scalp to clean the skin and remove dead skin cells, precisely position the electrodes, squirt electrode gel to get a good Software and Hardware
360 B.Z. Allison contact, check the impedance between the skin and the electrode, and repeat this process until impedances are reduced. Fortunately, there has been some progress toward improved sensors that require less or no gel [65] and may not require expert assistance. Several companies develop systems to play games, communicate, diagnose or monitor patients, and/or perform various functions using EEG. Some of these systems also measure other physiological signals. Such work should facilitate wider development of head-mounted physiological sensors, if not improved BCI electrodes. Users must perform intentional mental tasks to control any BCI. Different BCI approaches reflect different task alphabets. In some BCIs, imagination of left vs. right hand movement might convey information. Other BCIs cannot understand such neural signals, but instead allow communication via attention to visual targets. The term “alphabet” reflects that users must usually combine several BCI signals to send a message or accomplish another goal. Hybrid BCIs could detect activity stemming from both imagined movement and selective attention. Hybrid BCIs were suggested over 10 years ago [10], but were not developed until very recently, as discussed below. In the distant future, as BCIs learn to recognize larger alphabets, these mental tasks or “cognemes” might be compared to graphemes or phonemes in written or spoken language [4]. Of course, most written and spoken languages have vocabularies with many thousands of different elements. Modern BCIs recognize very few different signals. It is unlikely that BCIs will have alphabets comparable to natural languages for a very long time. However, there is some progress toward task alphabets that are larger and more consistent with natural languages. Some recent work describes a BCI that can identify some vowels and consonants that a user is imagining [31, 69]. This feat requires an invasive BCI. Efforts to reliably identify individual words or sentences through the EEG have failed [78]. While noninvasive BCIs will develop larger alphabets, a noninvasive system will never allow a vocabulary as large as a natural language without a dramatic revolution in neuroimaging technology. Similarly, a “literal” BCI that can directly translate all of your thoughts into messages or commands is very far from reality. Recent work has discussed BCIs based on new or better defined tasks, such as quasi-movements, first person movement imagery, or imagined music [23, 52, 56]. Brain activity relating to perceived error, image recognition, or other experiences might be used in other systems based on the EEG, perhaps combined with other signals [13, 18, 25, 29, 54]. Ideally, the mental tasks used in BCIs should be easy to generate, difficult to produce accidentally, and easily mapped on to desired commands. BCIs might also use existing tasks for novel goals. The last BCI Catalyst, Brain Effects, reflects changes that may occur in the brain or body because of BCI use. There could be negative side effects, which may be similar to the negative side effects of watching too much TV or playing too many computer games. However, some newer BCI systems are based on a revolutionary principle: using a BCI to produce positive effects. Although the user does send command and control signals, the system’s main goal is not to enable
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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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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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Brain-Computer Interfaces for Commu
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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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262 J. Mellinger and G. Schalk 2 BC
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264 J. Mellinger and G. Schalk Fig.
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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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Page 388 and 389: Toward Ubiquitous BCIs 383 physicis
Page 390 and 391: Toward Ubiquitous BCIs 385 31. F.H.
Page 392 and 393: Toward Ubiquitous BCIs 387 67. G. S
Page 394 and 395: 390 Index 65, 157, 163, 217, 221-23
Page 396 and 397: 392 Index 208, 236, 243, 245, 260-2
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