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Use of ANNs as Classifiers for Selective Attention Brain-Computer Interfaces.
- Source :
- Computational & Ambient Intelligence; 2007, p956-963, 8p
- Publication Year :
- 2007
-
Abstract
- Selective attention to visual-spatial stimuli causes decrements of power in alpha band and increments in beta. For steady-state visual evoked potentials (SSVEP) selective attention affects electroencephalogram (EEG) recordings, modulating the power in the range 8-27 Hz. The same behaviour can be seen for auditory stimuli as well, although for auditory steady-state response (ASSR), it is not fully confirmed yet. The design of selective attention based brain-computer interfaces (BCIs) has two major advantages: First, no much training is needed. Second, if properly designed, a steady-state response corresponding to spectral peaks can be elicited, easy to filter and classify. In this paper we study the behaviour of ANNs as classifiers for a selective attention to auditory stimuli based BCI system. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540730064
- Database :
- Complementary Index
- Journal :
- Computational & Ambient Intelligence
- Publication Type :
- Book
- Accession number :
- 33147791
- Full Text :
- https://doi.org/10.1007/978-3-540-73007-1_115