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

K. Chojnowski; J. Frączek

doi:10.1117/12.2000166

15 October 2012 EEG signal classification based on artificial neural networks and amplitude spectra features

K. Chojnowski, J. Frączek

Proceedings Volume 8454, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012; 84541Q (2012) https://doi.org/10.1117/12.2000166
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012, 2012, Wilga, Poland

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.

Citation Download 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 (15 October 2012); https://doi.org/10.1117/12.2000166

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