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Proceedings Paper

EEG signal classification based on artificial neural networks and amplitude spectra features
Author(s): K. Chojnowski; J. Frączek
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Paper Abstract

BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.

Paper Details

Date Published: 15 October 2012
PDF: 9 pages
Proc. SPIE 8454, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012, 84541Q (15 October 2012); doi: 10.1117/12.2000166
Show Author Affiliations
K. Chojnowski, Warsaw Univ. of Technology (Poland)
J. Frączek, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 8454:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012
Ryszard S. Romaniuk, Editor(s)

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