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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
López, M. Á., Pomares, H., Damas, M., Madrid, E., Prieto, A., Pelayo, F., & De La Plaza Hernández, E. M. (2007). Use of ANNs as classifiers for selective attention brain-computer interfaces. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4507 LNCS, pp. 956–963). Springer Verlag. https://doi.org/10.1007/978-3-540-73007-1_115
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