Brainâ€“Computer Interfaces - Index of

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96 G. Pfurtscheller et al. 51. G. Pfurtscheller, C. Neuper, C. Guger, W. Harkam, H. Ramoser, A. Schlögl, B. Obermaier, and M. Pregenzer, Current trends in Graz brain-computer interface (BCI) research. IEEE Transa Rehabil Eng, 8, 216–219, (2000). 52. G. Pfurtscheller, M. Wörtz, G. Supp, and F.H. Lopes da Silva, Early onset of post-movement beta electroencephalogram synchronization in the supplementary motor area during selfpaced finger movement in man. Neurosci Lett, 339, 111–114, (2003). 53. G. Pfurtscheller and T. Solis-Escalante, Could the beta rebound in the EEG be suitable to realize a “brain switch”? Clin Neurophysiol, 120, 24–29, (2009). 54. M. Pregenzer, G. Pfurtscheller, and D. Flotzinger, Automated feature selection with a distinction sensitive learning vector quantizer. Neurocomput, 11, 19–29, (1996). 55. P. Pudil, J. Novovičova, and J. Kittler, Floating search methods in feature selection. Pattern Recognit. Lett., 15, 1119–1125, (1994). 56. M. Le Van Quyen, Disentangling the dynamic core: a research program for a neurodynamics at the large-scale. Biol Res, 36, 67–88, (2003). 57. H. Ramoser, J. Müller-Gerking, and G. Pfurtscheller, Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng, 8, 441–446, (2000). 58. R. Scherer, Towards practical brain-computer interfaces: self-paced operation and reduction of the number of EEG sensors. PhD thesis, Graz University of Technology, (2008). 59. R. Scherer, G.R. Müller-Putz, and G. Pfurtscheller, Self-initiation of EEG-based braincomputer communication using the heart rate response. J Neural Eng, 4, L23–L29, (2007). 60. R. Scherer, A. Schlögl, F. Lee, H. Bischof, J. Janša, and G. Pfurtscheller, The self-paced Graz brain-computer interface: methods and applications. Comput Intell Neurosci, 2007, 79826, (2007). 61. A. Schlögl, D. Flotzinger, and G. Pfurtscheller, Adaptive autoregressive modeling used for single-trial EEG classification. Biomed Tech, 42, 162–167, (1997). 62. T. Solis-Escalante, G.R. Müller-Putz, and G. Pfurtscheller, Overt foot movement detection in one single Laplacian EEG derivation. J Neurosci Methods, 175(1), 148–153, (2008). 63. G. Supp, A. Schlögl, N. Trujillo-Barreto, M. M. Müller, and T. Gruber, Directed cortical information flow during human object recognition: analyzing induced EEG gamma-band responses in brain’s source space. PLoS ONE, 2, e684, (2007). 64. J. Talairach and P. Tournoux, Co-planar stereotaxic atlas of the human brain. NewYork,NY Thieme, 1998. 65. G. Townsend, B. Graimann, and G. Pfurtscheller, Continuous EEG classification during motor imagery – simulation of an asynchronous BCI. IEEE Trans Neural Sys Rehabil Eng, 12, 258– 265, (2004). 66. F.J. Varela, J.-P. Lachaux, E. Rodriguez, and J. Martinerie, The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci, 2, 229–239, (2001). 67. N. Weiskopf, K. Mathiak, S.W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans Biomed Eng, 51, 966–970, (2004). 68. J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, and T.M. Vaughan, Braincomputer interfaces for communication and controls. Clin Neurophysiol, 113, 767–791, (2002). 69. J.R. Wolpaw and D.J. McFarland, Multichannel EEG-based brain-computer communication. Electroencephalogr Clin Neurophysiol, 90, 444–449, (1994). 70. G. Pfurtscheller, B.Z. Allison, C. Brunner, G. Bauernfeind, T. Solis-Escalante, R. Scherer, T.O. Zander, G. Mueller-Putz, C. Neuper, and N. Birbaumer, The hybrid BCI. Front Neurosci, 4, 30, (2010). 71. G. Pfurtscheller, T. Solis Escalante, R. Ortner, P. Linortner, and G. Müller-Putz, Self-paced operation of an SSVEP-based orthosis with and without an imagery-based “brain switch”: a feasibility study towards a hybrid BCI. IEEE Trans Neural Syst Rehabil Eng, 18(4), 409–414, (2010). 72. G. Pfurtscheller, G. Bauernfeind, S. Wriessnegger, and C. Neuper, Focal frontal (de)oxyhemoglobin responses during simple arithmetic. Int J Psychophysiol, 76, 186–192, (2010).
BCIs in the Laboratory and at Home: The Wadsworth Research Program Eric W. Sellers, Dennis J. McFarland, Theresa M. Vaughan, and Jonathan R.Wolpaw 1 Introduction Many people with severe motor disabilities lack the muscle control that would allow them to rely on conventional methods of augmentative communication and control. Numerous studies over the past two decades have indicated that scalp-recorded electroencephalographic (EEG) activity can be the basis for non-muscular communication and control systems, commonly called brain–computer interfaces (BCIs) [55]. EEG-based BCI systems measure specific features of EEG activity and translate these features into device commands. The most commonly used features are rhythms produced by the sensorimotor cortex [38, 55, 56, 59], slow cortical potentials [4, 5, 23], and the P300 event-related potential [12, 17, 46]. Systems based on sensorimotor rhythms or slow cortical potentials use oscillations or transient signals that are spontaneous in the sense that they are not dependent on specific sensory events. Systems based on the P300 response use transient signals in the EEG that are elicited by specific stimuli. BCI system operation has been conceptualized in at least three ways. Blankertz et al. (e.g., [7]) view BCI development mainly as a problem of machine learning (see also chapter 7 in this book). In this view, it is assumed that the user produces a signal in a reliable and predictable fashion, and the particular signal is discovered by the machine learning algorithm. Birbaumer et al. (e.g., [6]) view BCI use as an operant conditioning task, in which the experimenter guides the user to produce the desired output by means of reinforcement (see also chapter 9 in this book). We (e.g., [27, 50, 57] see BCI operation as the continuing interaction of two adaptive controllers, the user and the BCI system, which adapt to each other. These three concepts of BCI operation are illustrated in Fig. 1. As indicated later in this review, mutual adaptation is critical to the success of our sensorimotor rhythm (SMR)-based BCI E.W. Sellers (B) Department of Psychology, East Tennessee State University, Box 70649, Johnson City, TN 37641, USA; Laboratory of Neural Injury and Repair, Wadsworth Center, New York State Department of Health, Albany, NY 12201-0509, USA e-mail: sellers@etsu.edu B. Graimann et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection, DOI 10.1007/978-3-642-02091-9_6, C○ Springer-Verlag Berlin Heidelberg 2010 97
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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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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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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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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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