| Comparison of adaptive features with linear discriminant classifier for Brain computer Interfaces. | |
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MedLine Citation:
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PMID: 19162621 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Many Brain-computer Interfaces (BCI) use band-power estimates with more or less subject-specific optimization of the frequency bands. However, a number of alternative EEG features do not need to select the frequency bands; estimators for these features have been modified for an adaptive use. The popular band power estimates were compared with Adaptive AutoRegressive parameters, Hjorth, Barlow, Wackermann, Brain-Rate and a new feature type called Time Domain Parameter. The results from 21 subjects show that several features provide an equally good or even better performance, while no subject-specific optimization is needed, and they are also preferable to band power when the most discriminating frequency band of a subject is not known. |
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Authors:
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Carmen Vidaurre; Alois Schlögl |
Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Volume: 2008 ISSN: 1557-170X ISO Abbreviation: Conf Proc IEEE Eng Med Biol Soc Publication Date: 2008 |
Date Detail:
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Created Date: 2009-02-16 Completed Date: 2009-05-07 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101243413 Medline TA: Conf Proc IEEE Eng Med Biol Soc Country: United States |
Other Details:
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Languages: eng Pagination: 173-6 Citation Subset: IM |
Affiliation:
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Intelligent Data Analysis Group, FIRST, Fraunhofer Institute, Kekulestr. 7, Berlin 12489, Germany. carmen.vidaurreATfirst.fraunhofer.de |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Brain / physiology* Computer Simulation Discriminant Analysis Electroencephalography / methods* Evoked Potentials, Motor / physiology* Humans Imagination / physiology* Linear Models Models, Neurological Models, Statistical Pattern Recognition, Automated / methods* Reproducibility of Results Sensitivity and Specificity User-Computer Interface* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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