Brainâ€“Computer Interfaces - Index of

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258 K.J. Miller and J.G. Ojemann 26. A. Brovelli, J.P. Lachaux, P. Kahane, and D. Boussaoud, High gamma frequency oscillatory activity dissociates attention from intention in the human premotor cortex. NeuroImage, 28(1), 154–164, (2005). 27. R.T. Canolty, et al., High gamma power is phase-locked to theta oscillations in human neocortex. Science, 313(5793), 1626–1628, (2006). 28. 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, (2008). 29. B. Blankertz, G. Dornhege, M. Krauledat, K.R. Muller, and G. Curio, The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects. Neuroimage, 37(2), 539–550, (2007). 30. S. Lemm, B. Blankertz, G. Curio, and K.R. Muller, Spatio-spectral filters for improving the classification of single trial EEG. IEEE Trans Biomed Eng, 52(9), 1541–1548, (2005). 31. K.R. Muller, et al., Machine learning for real-time single-trial EEG-analysis: From braincomputer interfacing to mental state monitoring. J Neurosci Methods, 167(1), 82–90, (2008). 32. P. Shenoy, K.J. Miller, J.G. Ojemann, and R.P. Rao, Generalized features for electrocorticographic BCIs. IEEE Trans Biomed Eng, 55(1), 273–280, (2008). 33. R. Scherer, B. Graimann, J.E. Huggins, S.P. Levine, and G. Pfurtscheller, Frequency component selection for an ECoG-based brain-computer interface. Biomed Tech (Berl), 48(1–2), 31–36, (2003). 34. B. Graimann, J.E. Huggins, S.P. Levine, and G. Pfurtscheller, Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data. Clin Neurophysiol, 113(1), 43–47, (2002). 35. T. Blakeley, K.J. Miller, S. Zanos, R.P.N. Rao, J.G. Ojemann: Robust long term control of an electrocorticographic brain computer interface with fixed parameters. Neurosurg Focus, 27(1), E13, (2009, Jul). 36. K.J. Miller, G.S. Schalk, E.E. Fetz, M. den Nijs, J.G. Ojemann, and R.P.N. Rao: Cortical activity during motor movement, motor imagery, and imagery-based online feedback. Proc Natl Acad Sci, 107(9), 4430–4435, (2010, Mar). 37. G. Schalk, et al., Two-dimensional movement control using electrocorticographic signals in humans. J Neural Eng, 5(1), 75–84, (2008). 38. K.J. Miller, et al., Three cases of feature correlation in an electrocorticographic BCI. IEEE Eng Med Biol Soc, 5318–5321, (2008).
Using BCI2000 in BCI Research Jürgen Mellinger and Gerwin Schalk 1 Introduction BCI2000 is a general-purpose system for brain–computer interface (BCI) research. It can also be used for data acquisition, stimulus presentation, and brain monitoring applications [18, 27]. The mission of the BCI2000 project is to facilitate research and applications in these areas. BCI2000 has been in development since 2000 in a collaboration between the Wadsworth Center of the New York State Department of Health in Albany, New York, and the Institute of Medical Psychology and Behavioral Neurobiology at the University of Tübingen, Germany. Many other individuals at different institutions world-wide have contributed to this project. BCI2000 has already had a substantial impact on BCI research. As of mid-2010, BCI2000 has been acquired by more than 350 laboratories around the world. The original article that described the BCI2000 system [27] has been cited more than 200 times, and was recently awarded the Best Paper Award by IEEE Transactions on Biomedical Engineering. Furthermore, a review of the literature revealed that BCI2000 has been used in more than 50 peer-reviewed publications. Many of these papers set new directions in BCI research, which include: the first online brain–computer interfaces using magnetoencephalographic (MEG) signals [19] or electrocorticographic (ECoG) signals [9, 13, 14, 28, 33]; the first application of BCI technology to functional restoration in patients with chronic stroke [4, 34]; the demonstration that non-invasive BCI systems can support multidimensional cursor movements [16, 17, 36]; the first real-time BCI use of high-resolution EEG techniques [6]; the use of BCI techniques to control assistive technologies [7]; control of a humanoid robot by a noninvasive BCI [2]; and the first demonstrations that people severely paralyzed by amyotrophic lateral sclerosis (ALS) can use BCIs based on sensorimotor rhythms or P300 evoked potentials [11, 22, 32]. In addition, several studies have used BCI2000 for purposes other than BCI research, e.g., the first large-scale motor mapping studies using ECoG signals [12, 21]; real-time J. Mellinger (B) Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany e-mail: juergen.mellinger@uni-tuebingen.de B. Graimann et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection, DOI 10.1007/978-3-642-02091-9_15, C○ Springer-Verlag Berlin Heidelberg 2010 259
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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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Detecting Mental States by Machine
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Non Invasive BCIs for Neuroprosthes
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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 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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