Brainâ€“Computer Interfaces - Index of

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Toward Ubiquitous BCIs 383 physicists could manage – even today – is destroying the earth. Nightmare scenarios of BCIs gone awry would leave the world intact while rotting humanity. Fortunately, the nightmare scenario seen in some bci fi – a phlegmatic Orwellian dystopia of pale, unfulfilled, blindly dependent masses – can be proactively prevented. Many mechanisms for prevention stem from mechanisms already in place with other new technologies. BCI researchers are aware of ethical issues, discuss them openly, and are committed to helping people. We can have at least some confidence that parents, doctors, psychiatrists, and end users will generally behave ethically when choosing or recommending BCIs. People may revolutionize BCIs – many times – but BCIs will remain a minor part of humanity. Humanity may be changing technology, but that doesn’t necessarily make us less human. Acknowledgments This paper was supported in part by three grants: a Marie Curie European Transfer of Knowledge grant Brainrobot, MTKD-CT-2004-014211, within the 6th European Community Framework Program; the Information and Communication Technologies Coordination and Support action “FutureBNCI”, Project number ICT-2010-248320; and the Information and Communication Technologies Collaborative Project action “BrainAble”, Project number ICT- 2010-247447. Thanks to Sara Carro-Martinez and Drs. Alida Allison, Clemens Brunner, Gary Garcia, Gaye Lightbody, Paul McCullagh, Femke Nijboer, Kai Miller, Jaime Pineda, Gerwin Schalk, and Jonathan Wolpaw for comments on this manuscript or on some ideas herein. The sentence about “fardels bear” is paraphrased from Hamlet. References 1. B.Z. Allison, C. Brunner, S. Grissmann, and C. Neuper (2010). Toward a multidimensional “hybrid” BCI based on simultaneous SSVEP and ERD activity. Program No. 227.4. 2010 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2010. Online. Presentation accepted and scheduled for Nov 2010. 2. B.Z. Allison, D. Valbuena, T. Lueth, A. Teymourian, I. Volosyak, and A. Gräser, BCI demographics: How many (and what kinds of) people can use an SSVEP BCI? IEEE Trans Neural Syst Rehabil Eng, 18(2), 107–116, (2010). 3. B.Z. Allison, Human-computer interaction: Novel interaction methods and techniques, chapter The I of BCIs: next generation interfaces for brain – computer interface systems that adapt to individual users. Springer, New York, (2009). 4. B.Z. Allison, J.B. Boccanfuso, C. Agocs, L.A. McCampbell, D.S. Leland, C. Gosch, and M. Moore Jackson, Sustained use of an SSVEP BCI under adverse conditions. Cogn Neurosci Soc, 129, (2006). 5. B.Z. Allison, C. Brunner, V. Kaiser, G. Müller-Putz, C. Neuper, and G. Pfurtscheller. A hybrid brain-computer interface based on imagined movement and visual attention. J Neural Eng, 7(2), 26007, (2010). 6. B.Z. Allison, D.J. McFarland, G. Schalk, S.D. Zheng, M.M. Jackson, and J.R. Wolpaw, Towards an independent brain–computer interface using steady state visual evoked potentials. Clin Neurophysiol, 119, 399–408, (2008). 7. B.Z. Allison, and M.M. Moore (2004). Field validation of a P3 BCI under adverse conditions. Society for Neuroscience Conference. Program No. 263.9. San Diego, CA. 8. B.Z. Allison and C. Neuper, Could anyone use a BCI? In D.S. Tan and A. Nijholt, (Eds.), (B+H)CI: The human in brain–computer interfaces and the brain in human-computer interaction, volume in press. Springer, New York, (2010). 9. B.Z. Allison and J.A. Pineda, Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: Implications for a BCI system. Int J Psychophysiol, 59, 127–140, (2006).
384 B.Z. Allison 10. B.Z. Allison, A. Vankov, and J.A. Pineda., EEGs and ERPs associated with real and imagined movement of single limbs and combinations of limbs and applications to brain computer interface (BCI) systems. Soc Neurosci Abs, 25, 1139, (1999). 11. B.Z. Allison, E.W. Wolpaw, and J.R. Wolpaw, Brain–computer interface systems: progress and prospects. Expert Rev Med Devices, 4, 463–474, (2007). 12. C. Brunner, B.Z. Allison, C. Altstätter, and C. Neuper (2010). A hybrid brain-computer interface based on motor imagery and steady-state visual evoked potentials. Program No. 227.3. 2010 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2010. Online. Presentation accepted and scheduled for Nov 2010. 13. N. Bigdely-Shamlo, A. Vankov, R.R. Ramirez, and S. Makeig, Brain activity-based image classification from rapid serial visual presentation. IEEE Trans Neural Syst Rehabil Eng, 16(5), 432–441, Oct (2008). 14. N. Birbaumer and L. G. Cohen, brain–computer interfaces: communication and restoration of movement in paralysis. J Physiol, 579, 621–636, (2007). 15. N. Birbaumer, N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. Kübler, J. Perelmouter, E. Taub, and H. Flor, A spelling device for the paralysed. Nature, 398, 297–298, (1999). 16. T. Blakely, K.J. Miller, S.P. Zanos, R.P.N. Rao, and J.G. Ojemann, Robust, long-term control of an electrocorticographic brain–computer interface with fixed parameters. Neurosurg Focus, 27(1), E13, Jul (2009). 17. C. Brunner, B.Z. Allison, D.J. Krusienski, V. Kaiser, G.R. Müller-Putz, C. Neuper, and G. Pfurtscheller Improved signal processing approaches for a hybrid brain-computer interface simulation. J Neurosci Method, 188(1),165–73. 18. A. Buttfield, P. W. Ferrez, and J. del R. Millán, Towards a robust BCI: Error potentials and online learning. IEEE Trans Neural Syst Rehabil Eng, 14, 164–168, 2006. 19. J.M. Carmena, M.A. Lebedev, R.E. Crist, J.E. O’Doherty, D.M. Santucci, D.F. Dimitrov, P.G. Patil, C.S. Henriquez, and M.A.L. Nicolelis, Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol, 1, E42, (2003). 20. F. Cincotti, D. Mattia, F. Aloise, S. Bufalari, G. Schalk, G. Oriolo, A. Cherubini, M.G. Marciani, and F. Babiloni, Non-invasive brain–computer interface system: towards its application as assistive technology. Brain Res Bull, 75(6), 796–803, Apr (2008). 21. L. Citi, R. Poli, C. Cinel, and F. Sepulveda, P300-based BCI mouse with genetically-optimized analogue control. IEEE Trans Neural Syst Rehabil Eng, 16, 51–61, (2008). 22. J. J. Daly and J. R. Wolpaw, brain–computer interfaces in neurological rehabilitation. Lancet Neurol, 7, 1032–1043, (2008). 23. B.J. de Kruif, R. Schaefer, and P. Desain, Classification of imagined beats for use in a brain computer interface. Conf Proc IEEE Eng Med Biol Soc, 2007, 678–681, (2007). 24. B. H. Dobkin, brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol, 579, 637–642, (2007). 25. P. W. Ferrez and J. del R. Millán, Error-related EEG potentials generated during simulated brain–computer interaction. IEEE Trans Biomed Engi, 55, 923–929, 2008. 26. F. Galán, M. Nuttin, E. Lew, P. W. Ferrez, G. Vanacker, J. Philips, and J. del R. Millán, A brain-actuated wheelchair: asynchronous and non-invasive brain?computer interfaces for continuous control of robots. Clin Neurophysiol, 119, 2159–2169, 2008. 27. X. Gao, D. Xu, M. Cheng, and S. Gao, A BCI-based environmental controller for the motiondisabled. IEEE Trans Neural Syst Rehabil Eng, 11, 137–140, 2003. 28. T. Geng, J. Q. Gan, M. Dyson, C. S. Tsui, and F. Sepulveda, A novel design of 4-class BCI using two binary classifiers and parallel mental tasks. Comput Intell Neurosci, 2008, 437306, (2008). 29. A. D. Gerson, L. C. Parra, and P. Sajda, Cortically coupled computer vision for rapid image search. IEEE Trans Neural Syst Rehabil Eng, 14(2), 174–179, Jun (2006). 30. Vora, J.Y., Allison, B.Z., & Moore, M.M. (2004). A P3 brain computer interface for robot arm control. Society for Neuroscience Abstract, 30, Program No. 421.19.
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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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52 G. Pfurtscheller and C. Neuper r
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54 G. Pfurtscheller and C. Neuper 6
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56 G. Pfurtscheller and C. Neuper B
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66 C. Neuper and G. Pfurtscheller s
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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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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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164 N. Birbaumer and P. Sauseng of
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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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204 D.M. Taylor and M.E. Stetner mo
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206 D.M. Taylor and M.E. Stetner el
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208 D.M. Taylor and M.E. Stetner ac
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210 D.M. Taylor and M.E. Stetner de
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212 D.M. Taylor and M.E. Stetner Ma
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214 D.M. Taylor and M.E. Stetner pe
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216 D.M. Taylor and M.E. Stetner ev
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218 D.M. Taylor and M.E. Stetner fo
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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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250 K.J. Miller and J.G. Ojemann fo
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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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272 J. Mellinger and G. Schalk reco
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274 J. Mellinger and G. Schalk Fig.
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276 J. Mellinger and G. Schalk numb
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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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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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