
Proceedings Paper
EEG signal classification based on artificial neural networks and amplitude spectra featuresFormat | Member Price | Non-Member Price |
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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
Published in SPIE Proceedings Vol. 8454:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012
Ryszard S. Romaniuk, Editor(s)
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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