Discriminative Methods for Classification of Asynchronous Imaginary Motor Tasks From EEG Data

In this work, two methods based on statistical models that take into account the temporal changes in the electroencephalographic (EEG) signal are proposed for asynchronous brain-computer interfaces (BCI) based on imaginary motor tasks. Unlike the current approaches to asynchronous BCI systems that m...

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Veröffentlicht in:	IEEE transactions on neural systems and rehabilitation engineering Jg. 21; H. 5; S. 716 - 724
Hauptverfasser:	Delgado Saa, Jaime F., Cetin, Mujdat
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2013
Schlagworte:	Algorithms Brain modeling brain states Brain-computer interface (BCI) Brain-Computer Interfaces - classification Computer Graphics conditional random fields (CRFs) Data Interpretation, Statistical Data models discriminative models Electroencephalography Electroencephalography - classification Electroencephalography - statistics & numerical data Hidden Markov models Humans imaginary motor tasks Imagination - classification Imagination - physiology Labeling Linear Models Models, Neurological Motor Skills - classification Motor Skills - physiology Psychomotor Performance - physiology sensorimotor rhythms sequential labeling Task analysis Training User-Computer Interface
ISSN:	1534-4320, 1558-0210, 1558-0210
Online-Zugang:	Volltext
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