Brainâ€“Computer Interfaces - Index of

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BCIs Based on Signals from Between the Brain and Skull 239 39. J.E. Huggins, R.C. Welsh, V. Swaminathan, et al., fMRI for the prediction of direct brain interface recording location. Biomed Technik, 49, 5–10, (2004). 40. G. Schalk, D.J. McFarland, T. Hinterberger, N. Birbaumer, J.R. Wolpaw, BCI2000: A generalpurpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng, 51, 1034–1043, (2004). 41. E.C. Leuthardt, K.J. Miller, G. Schalk, R.P. Rao, J.G. Ojemann, Electrocorticography-based brain computer interface – the seattle experience. IEEE Trans Neural Syst Rehabil Eng, 14, 194–198, (2006). 42. G. Schalk, J. Kubanek, K.J. Miller, et al., Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J Neural Eng, 4, 264–275, (2007). 43. T.N. Lal, T. Hinterberger, G. Widman, M. Schroder, N.J. Hill, W. Rosenstiel, C.E. Elger, B. Scholkopf, N. Birbaumer, Methods towards invasive human brain computer interfaces. In: K.Saul,Y.Weiss,andL.Bottou(Eds.),Advances in neural information processing systems, MIT Press, Cambridge, MA, pp. 737–744, (2005). 44. N.J. Hill, T.N. Lal, M. Schroder, et al., Classifying EEG and ECoG signals without subject training for fast BCI implementation: Comparison of nonparalyzed and completely paralyzed subjects. IEEE Trans Neural Syst Rehabil Eng, 14, 183–186, (2006). 45. B. Wilhelm, M. Jordan, N. Birbaumer, Communication in locked-in syndrome: Effects of imagery on salivary pH. Neurology, 67, 534–535, (2006). 46. N.F. Ramsey, M.P. van de Heuvel, K.H. Kho, F.S.S. Leijten, Towards human BCI applications based on cognitive brain systems: An investigation of neural signals recorded from the dorsolateral prefrontal cortex. IEEE Trans Neural Syst Rehabil Eng, 14, 214–217, (2006). 47. J.P. Lachaux, K. Jerbi, O. Bertrand, et al., A blueprint for real-time functional mapping via human intracranial recordings. PLoS ONE, 2, e1094, (2007).
A Simple, Spectral-Change Based, Electrocorticographic Brain–Computer Interface Kai J. Miller and Jeffrey G. Ojemann 1 Introduction A brain–computer interface (BCI) requires a strong, reliable signal for effective implementation. A wide range of real-time electrical signals have been used for BCI, ranging from scalp recorded electroencephalography (EEG) (see, for example, [1, 2]) to single neuron recordings (see, for example, [3, 4]. Electrocorticography (ECoG) is an intermediate measure, and refers to the recordings obtained directly from the surface of the brain [5]. Like EEG, ECoG represents a population measure, the electrical potential that results from the sum of the local field potentials resulting from 100,000 s of neurons under a given electrode. However, ECoG is a stronger signal and is not susceptible to the artifacts from skin and muscle activity that can plague EEG recordings. ECoG and EEG also differ in that the phenomena they measure encompass fundamentally different scales. Because ECoG electrodes lie on the cortical surface, and because the dipole fields [7] that produce the cortical potentials fall off rapidly (V(r) ∼ r −2 ), the ECoG fundamentally reflects more local processes. Currently, ECoG takes place in the context of clinical recording for the treatment of epilepsy. After implantation, patients recover in the hospital while they wait to have a seizure. Often, that requires a week or longer of observation, during which time patients may chose to participate in experiments relevant to using ECoG to drive BCI. Recently, researchers have used the spectral changes on the cortical surface of these patients to provide feedback, creating robust BCIs, allowing individuals to control a cursor on a computer screen in a matter of minutes [8–12]. This chapter discusses the important elements in the construction of these ECoG based BCIs: signal acquisition, feature selection, feedback, and learning. K.J. Miller (B) Department of Physics, Neurobiology, and Behavior, University of Washington, Seattle, WA 98195, USA e-mail: kjmiller@u.washington.edu B. Graimann et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection, DOI 10.1007/978-3-642-02091-9_14, C○ Springer-Verlag Berlin Heidelberg 2010 241
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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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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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84 G. Pfurtscheller et al. A B C 1
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98 E.W. Sellers et al. Fig. 1 Three
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108 E.W. Sellers et al. 5 SMR-Based
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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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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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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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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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