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Proceedings Article

EEG signal classification based on artificial neural networks and amplitude spectra features

[+] Author Affiliations
K. Chojnowski, J. Frączek

Warsaw Univ. of Technology (Poland)

Proc. SPIE 8454, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012, 84541Q (October 15, 2012); doi:10.1117/12.2000166
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From Conference Volume 8454

  • Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012
  • Ryszard S. Romaniuk
  • Wilga, Poland | May 28, 2012

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. © (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

K. Chojnowski and J. Frączek
" EEG signal classification based on artificial neural networks and amplitude spectra features ", Proc. SPIE 8454, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012, 84541Q (October 15, 2012); doi:10.1117/12.2000166; http://dx.doi.org/10.1117/12.2000166


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