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Comparison of adaptive features with linear discriminant classifier for Brain computer Interfaces

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2 Author(s)
Vidaurre, Carmen ; Intelligent Data Analysis Group at FIRST, Fraunhofer Institute, Kekulestr. 7, Berlin 12489, Germany ; Schlogl, Alois

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.

Published in:

Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE

Date of Conference:

20-25 Aug. 2008

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