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

被引：0
|
作者：
Chojnowski, K. [1 ]
Fraczek, J. [2 ]
机构：
[1] Warsaw Univ Technol, Fac Elect & Informat Technol, Inst Radioelect, Warsaw, Poland
[2] Warsaw Univ Technol, Fac Elect & Informat Technol, Inst Elect Syst, Warsaw, Poland
来源：
PHOTONICS APPLICATIONS IN ASTRONOMY, COMMUNICATIONS, INDUSTRY, AND HIGH-ENERGY PHYSICS EXPERIMENTS 2012 | 2012年 / 8454卷
关键词：
BCI; EEG signal; artificial neural networks; RProp algorithm; classification; multithreaded programming; biological signals; hand movements; ears stimulation; eyes stimulation; ARCHITECTURE; PRINCIPLES; EMERGENCE; CELLS;
D O I：
10.1117/12.2000166
中图分类号：
P1 [天文学];
学科分类号：
0704 ;
摘要：
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.
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页数：9
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