Brainâ€“Computer Interfaces - Index of

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64 G. Pfurtscheller and C. Neuper 94. G. Pfurtscheller, C. Guger, G. Müller, et al., Brain oscillations control hand orthosis in a tetraplegic, Neuroscience Letters, 292, 211–214, (2000). 95. G. Pfurtscheller, G.R. Müller, J. Pfurtscheller, et al., “Thought”-control of functional electrical stimulation to restore handgrasp in a patient with tetraplegia, Neurosci Lett, 351, 33–36, (2003). 96. G.R. Müller-Putz, R. Scherer, G. Pfurtscheller, et al., EEG-based neuroprosthesis control: a step towards clinical practice, Neurosci Lett, 382, 169–174, (2005). 97. C. Neuper, R. Scherer, S. Wriessnegger, and G. Pfurtscheller. Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. Clin. Neurophysiol, 121(8), 239–247, (2009).
Neurofeedback Training for BCI Control Christa Neuper and Gert Pfurtscheller 1 Introduction Brain–computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2–4]. However, even with the best algorithms, successful BCI operation depends significantly on how well users can voluntarily modulate their neural activity. People need to produce brain signals that are easy to detect. This may be particularly important during “real-world” device control, when background mental activity and other electrical noise sources often fluctuate unpredictably. Learning to operate many BCI-controlled devices requires repeated practice with feedback and reward. BCI training hence engages learning mechanisms in the brain. Therefore, research that explores BCI training is important, and could benefit from existing research involving neurofeedback and learning. This research should consider the specific target application. For example, different training protocols and feedback techniques may be more or less efficient depending on whether the user’s task is to control a cursor on a computer screen [5–7], select certain characters or icons for communication [8–11], or control a neuroprosthesis to restore grasping [12, 13]. Even though feedback is an integral part of any BCI training, only a few studies have explored how feedback affects the learning process, such as when people learn to use their brain signals to move a cursor to a target on a computer C. Neuper (B) Department of Psychology, University of Graz, Graz, Austria; Institute for Knowledge Discovery, Graz University of Technology, Graz, Austria e-mail: christa.neuper@uni-graz.at B. Graimann et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection, DOI 10.1007/978-3-642-02091-9_4, C○ Springer-Verlag Berlin Heidelberg 2010 65
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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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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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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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168 N. Birbaumer and P. Sauseng 8.
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Non Invasive BCIs for Neuroprosthes
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BCIs Based on Signals from Between
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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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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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