Brainâ€“Computer Interfaces - Index of

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Toward Ubiquitous BCIs 387 67. G. Santhanam, S.I. Ryu, B.M. Yu, A. Afshar, and K.V. Shenoy, A high-performance brain– computer interface. Nature, 442, 195–198, (2006). 68. G. Schalk. brain–computer symbiosis. J Neural Eng, 5, P1–P15, (2008). 69. G. Schalk, X. Pei, N. Anderson, K. Wisneski, M.D. Smyth, W. Kim, D.L. Barbour, J.R. Wolpaw, and E.C. Leuthardt, Decoding spoken and imagined phonemes using electrocorticographic (ECoG) signals in humans: Initial data. Program No. 778.6. 2008 Abstract Viewer/Itinerary Planner. Society for Neuroscience, Washington, DC, (2008), Online. 70. G. Schalk, P. Brunner, L. A. Gerhardt, H. Bischof, and J. R. Wolpaw, brain–computer interfaces (BCIs): detection instead of classification. J Neurosci Methods, 167, 51–62, (2008). 71. G. Schalk, K.J. Miller, N.R. Anderson, J.A. Wilson, M.D. Smyth, J.G. Ojemann, D.W. Moran, J.R. Wolpaw, and E.C. Leuthardt, Two-dimensional movement control using electrocorticographic signals in humans. J Neural Eng, 5, 75–84, (2008). 72. R. Scherer, F. Lee, A. Schlögl, R. Leeb, H. Bischof, and G. Pfurtscheller, Toward self-paced brain–computer communication: navigation through virtual worlds. IEEE Trans Biomed Eng, 55, 675–682, (2008). 73. R. Scherer, G.R. Müller-Putz, and G. Pfurtscheller, Self-initiation of EEG-based brain– computer communication using the heart rate response. J Neural Eng, 4, L23–L29, (2007). 74. R. Scherer, G.R. Müller-Putz, and G. Pfurtscheller, Flexibility and practicality: Graz brain– computer interface approach. Int Rev Neurobiol, 86, 119–131, (2009). 75. E. W. Sellers, A. Kübler, and E. Donchin, brain–computer interface research at the University of South Florida Cognitive Psychophysiology Laboratory: The P300 speller. IEEE Trans Neural Syst Rehabil Eng, 14, 221–224, (2006). 76. R.M. Shiffrin and W. Schneider, Automatic and controlled processing revisited. Psychol Rev, 91, 269–276, 1984. 77. R. Sitaram, A. Caria, and N. Birbaumer, Hemodynamic brain–computer interfaces for communication and rehabilitation. Neural Netw, 22, 1320–1328, (2009). 78. P. Suppes, Z. L. Lu, and B. Han, Brain wave recognition of words. Proc Nat Acad Sci U S A, 94(26), 14965–14969, Dec (1997). 79. L.J. Trejo, N.J. McDonald, R. Matthews, and B.Z. Allison (2007) Experimental design and testing of a multimodal cognitive overload classifier. Automated Cognition International Conference, Baltimore, Maryland. Winner, best paper award. The conference was from 22–27 July: http://www.augmentedcognition.org/events.htm. 80. T.M. Vaughan, D.J. McFarland, G. Schalk, W.A. Sarnacki, D.J. Krusienski, E.W. Sellers, and J.R. Wolpaw, The Wadsworth BCI Research and Development Program: at home with BCI. IEEE Trans Neural Syst Rehabil Eng, 14(2), 229–233, Jun (2006). 81. 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. 82. J.R. Wolpaw, brain–computer interfaces as new brain output pathways. J Physiol, 579, 613– 619, (2007). 83. J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, and T.M. Vaughan, brain– computer interfaces for communication and control. Clin Neurophysiol, 113, 767–791, 2002. 84. J.R. Wolpaw, G.E. Loeb, B.Z. Allison, E. Donchin, O. Feix do Nascimento, W.J. Heetderks, F. Nijboer, W.G. Shain, and J.N. Turner, BCI Meeting 2005–workshop on signals and recording methods. IEEE Trans Neural Syst Rehabil Eng, 14(2), 138–141, Jun (2006). 85. J.R. Wolpaw and D.J. McFarland, Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans. Proc Nat Acad Sci USA, 101, 17849–17854, (2004).
Index A AAR paramter, adaptive autoregressive parameter, 282 Action potential (AP), 31, 37–38, 65, 157, 172–173, 205–208, 213, 216–217 Adaptive autoregressive parameter (AAR), 282, 339–341, 344–349 Alpha rhythm, alpha band rhythm, alpha oscillation, alpha band, 47–49, 52–53, 67, 70, 138, 146, 156, 337 Amyotrophic lateral sclerosis (ALS), 5, 16, 29, 35, 40, 158, 166–167, 186–195, 197–198, 230, 259, 377–378 AR parameters, 86, 339–341, 345–346 Artefact, 163, 174, 197 Asynchronous BCI, self-paced BCI, 14, 80, 85, 91, 146, 323–324 Attention deficit hyperactivity disorder (ADHD), 22, 66 Autoregressive model (AR), 86, 250, 315–316, 333, 339–341, 346–347, 352 B Band power, 50, 68, 70, 83, 86–87, 90, 116–118, 123–124, 130, 177–180, 246, 294, 315, 317, 346 BCI2000, 107, 233–234, 243, 253, 255, 259–278, 289–290, 363, 366, 368 BCI training, 50–51, 65–66, 70, 75, 88, 158–159, 161, 163, 177–179, 186, 190, 194–197, 293–295 Beta rebound, 54–55, 59, 84–85 Beta rhythm, beta band rhythm, beta oscillation, beta band, 21, 31–32, 34, 36, 47–49, 54–55, 57, 59, 66–67, 70, 72, 81, 83–84, 90–91, 98, 100–101, 137–139, 261, 282, 294, 306, 313, 315 Bipolar recording, 70, 90, 177 Blind source separation (BSS), 311 Blood oxygenation level dependent (BOLD), 7, 53, 84, 92, 119, 164–166 Brain oscillations, 11, 81 C Center-out task, 37, 211, 215, 218 Central nervous system (CNS), 3–4, 30–31, 166, 172 Channel selection, 317 Classification, classifier, 13–15, 36, 48, 56–57, 65, 69–72, 74–75, 79–83, 85–88, 91–92, 98, 100–108, 114, 116–118, 127, 130, 143, 146, 149, 155, 166, 177, 179, 181, 223–224, 262, 273, 282, 284, 290, 292, 295, 298, 314, 316, 318–319, 321–324, 331–333, 341–345, 347, 349, 352 Cochlear implant, 4 Common average reference (CAR), 288, 307–309, 315 Common mode rejection ratio (CMRR), 288 Common spatial patterns (CSP), 56, 86–87, 116–117, 147, 286, 295, 307, 312–313 Completely locked-in state (CLIS), 158, 167, 188–191 Cross-validation, 130, 149, 321–323, 347–348 D Data acquisition, 80, 259, 261–263, 275, 283–284, 290, 292, 294–295, 305–306 2D control, 100, 254, 256 3D control, 102, 108, 214 Desynchronization, 11, 36, 47–50, 52–54, 57–58, 70, 79, 81, 98, 116, 141, 177, 231, 245–246, 249, 294, 306 E Electrocardiogram, electrocardiography (ECG), 288–289 Electrocorticogram, electrocorticography (ECoG), 8–9, 22, 31, 34, 37, 39–40, 48, B. Graimann et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection, DOI 10.1007/978-3-642-02091-9, C○ Springer-Verlag Berlin Heidelberg 2010 389
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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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332 A. Schlögl et al. Fig. 1 Schem
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334 A. Schlögl et al. For the rect
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Page 384 and 385: Toward Ubiquitous BCIs 379 conversa
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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 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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