Brainâ€“Computer Interfaces - Index of

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256 K.J. Miller and J.G. Ojemann areas are becoming more pronounced), and the overall scale (to the top right) is increasing. Similar one dimensional tasks were performed for tongue and hand. The hand movement was coupled to left-right cursor movement, in preparation for the combination of the two into a two dimensional task. Figure 7c, Two-Dimensional Cursor Feedback: In the last stage of the experiment, two one dimensional control signals are combined into a single cursor to target task. If the two signals are independent, as is the case here, then the transition between robust control in two one-dimensional tasks and robust control in one two-dimensional task is straightforward. The combination of hand and tongue linked features is good (as in (c)), because they are well demarcated on the precentral gyrus, but the pair chosen in any particular instance will be dependent on the coverage of the electrode array. The example shown in (c), with the frequency range 80–90 Hz demonstrates how robust, screened, features (left) can be used for robust control in one-dimensional tasks (center). The electrode for up-down control in both the one- and two-dimensional tasks was from the classic tongue area. The electrode for left-right control in both the one- and two-dimensional tasks was from the classic hand area. The one- and two-dimensional control tasks were successful (100% Left/Right 1D, 97% Up/Down 1D, and 84% 2D). 7 Conclusion ECoG provides robust signals that can be used in a BCI system. Using different kinds of motor imagery, subjects can volitionally control the cortical spectrum in multiple brain areas simultaneously. The experimenter can identify salient brain areas and spectral ranges using a cue based screening task. These different kinds of imagery can be coupled to the movement of a cursor on a screen in a feedback process. By coupling them first separately, and later in concert, the subject can learn to control multiple degrees of freedom simultaneously in a cursor based task. Acknowledgements Special thanks to Gerwin Schalk for his consistent availability and insight. The patients and staff at Harborview Medical Center contributed invaluably of their time and enthusiasm. Author support includes NSF 0130705 and NIH NS07144. References 1. J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, and T.M. Vaughan, Braincomputer interfaces for communication and control. Clin Neurophysiol, 113(6), 767–791, (2002). 2. J.R. Wolpaw and D.J. McFarland, Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci USA, 101(51), 17849–17854, (2004). 3. L.R. Hochberg, et al., Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099), 164–171, (2006). 4. P.R. Kennedy and R.A. Bakay, Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport, 9(8), 1707–1711, (1998).
A Simple, Spectral-Change Based, Electrocorticographic Brain–Computer Interface 257 5. J.G. Ojemann, E.C. Leuthardt, and K.J. Miller, Brain-machine interface: Restoring neurological function through bioengineering. Clin Neurosurg, 54(28), 134–136, (2007). 6. K.J. Miller, et al., Spectral changes in cortical surface potentials during motor movement. J Neurosci, 27(9), 2424–2432, (2007). 7. P.L. Nunez and B.A. Cutillo, Neocortical dynamics and human EEG rhythms, Oxford University Press, New York, pp. xii, 708 p., (1995). 8. E.C. Leuthardt, K.J. Miller, G. Schalk, R.P. Rao, and J.G. Ojemann, Electrocorticographybased brain computer interface – the Seattle experience. IEEE Trans Neural Syst Rehabil Eng, 14(2), 194–198, (2006). 9. E.C. Leuthardt, G. Schalk, J.R. Wolpaw, J.G. Ojemann, and D.W. Moran, A brain-computer interface using electrocorticographic signals in humans. J Neural Eng, 1(2), 63–71, (2004). 10. G. Schalk, et al., Two-dimensional movement control using electrocorticographic signals in humans. J Neural Eng, 5(1), 75–84, (2008). 11. E.A. Felton, J.A. Wilson, J.C. Williams, and P.C. Garell, Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases. J Neurosurg, 106(3), 495–500, (2007). 12. J.A. Wilson, E.A. Felton, P.C. Garell, G. Schalk, and J.C. Williams, ECoG factors underlying multimodal control of a brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 14(2), 246–250, (2006). 13. K.J. Miller, S. Zanos, E.E. Fetz, M. den Nijs, and J.G. Ojemann, Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans. J Neurosci, 29(10), 3132, (2009). 14. T. Blakely, K.J. Miller, R.P.N. Rao, M.D. Holmes, and J.G. Ojemann, Localization and classification of phonemes using high spatial resolution electrocorticography (ECoG) grids.Proc IEEE Eng Med Biol Soc, 4964–4967, (2008). 15. G. Schalk, D.J. McFarland, T. Hinterberger, N. Birbaumer, and J.R. Wolpaw, BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng, 51(6), 1034–1043, (2004). 16. K.J. Miller, et al., Cortical electrode localization from X-rays and simple mapping for electrocorticographic research: The “location on cortex” (LOC) package for MATLAB. J Neurosci Methods, 162(1–2), 303–308, (2007). 17. K.J. Miller, et al., Beyond the gamma band: The role of high-frequency features in movement classification. IEEE Trans Biomed Eng, 55(5), 1634–1637, (2008). 18. F. Aoki, E.E. Fetz, L. Shupe, E. Lettich, and G.A. Ojemann, Increased gamma-range activity in human sensorimotor cortex during performance of visuomotor tasks. Clin Neurophysiol, 110(3), 524–537, (1999). 19. N.E. Crone, D.L. Miglioretti, B. Gordon, and R.P. Lesser, Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. Brain, 121(Pt 12), 2301–2315, (1998). 20. N.E. Crone, et al., Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. I. Alpha and beta event-related desynchronization. Brain, 121(Pt 12), 2271–2299, (1998). 21. G. Pfurtscheller, B. Graimann, J.E. Huggins, S.P. Levine, and L.A. Schuh, Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement. Clin Neurophysiol, 114(7), 1226–1236, (2003). 22. G. Pfurtscheller, Event-related desynchronization (erd) and event related synchronization (ERS), Williams and Wilkins, Baltimore, MD, pp. 958–967, (1999). 23. K.J. Miller, L.B. Sorensen, J.G. Ojemann, M. den Nijs: Power-law scaling in the brain surface electric potential. PLOS Comput Biol., 5(12), e1000609, (2009, Dec). 24. C. Neuper, R. Scherer, M. Reiner, and G. Pfurtscheller, Imagery of motor actions: Differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Brain Res, 25(3), 668–677, (2005). 25. K.J. Miller, et al., Real-time functional brain mapping using electrocorticography. NeuroImage, 37(2), 504–507, (2007).
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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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56 G. Pfurtscheller and C. Neuper B
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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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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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Page 230 and 231: BCIs Based on Signals from Between
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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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