Titlebook: Brain-Computer Interfaces; Revolutionizing Huma Bernhard Graimann,Gert Pfurtscheller,Brendan Allis Book 2010 Springer-Verlag GmbH Germany,

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書目名稱Brain-Computer Interfaces影響因子(影響力)




書目名稱Brain-Computer Interfaces影響因子(影響力)學(xué)科排名




書目名稱Brain-Computer Interfaces網(wǎng)絡(luò)公開度




書目名稱Brain-Computer Interfaces網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Brain-Computer Interfaces被引頻次




書目名稱Brain-Computer Interfaces被引頻次學(xué)科排名




書目名稱Brain-Computer Interfaces年度引用




書目名稱Brain-Computer Interfaces年度引用學(xué)科排名




書目名稱Brain-Computer Interfaces讀者反饋




書目名稱Brain-Computer Interfaces讀者反饋學(xué)科排名

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,Brain Signals for Brain–Computer Interfaces,principles, and outlines a set of brain signals appropriate for BCI use. Section 2 describes specific brain signals used in BCIs, their neurophysiological origins, and their current applications. Finally, Sect. 3 discusses issues critical for maximizing the effectiveness of BCIs.

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Dynamics of Sensorimotor Oscillations in a Motor Task,re, many people can use BCI systems by imagining movements to convey information. The EEG has many regular rhythms. The most famous are the occipital alpha rhythm and the central mu and beta rhythms. People can desynchronize the alpha rhythm (that is, produce weaker alpha activity) by being alert, a

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Neurofeedback Training for BCI Control,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 neu

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The Graz Brain-Computer Interface,[36, 38] of single-trial electroencephalographic (EEG) data during actual (overt) and imagined (covert) hand movement [9, 18, 40]. At the beginning of our BCI research activities we had a cooperation with the Wadsworth Center in Albany, New York State, USA, with the common interest to control one-di

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BCIs in the Laboratory and at Home: The Wadsworth Research Program, 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 featur

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,Detecting Mental States by Machine Learning Techniques: The Berlin Brain–Computer Interface,ized for revealing the user’s mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2–5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specif

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