Abstract
Analysis and classification of Electroencephalography (EEG) Data are still a big challenge. This kind if data is very sensitive and complex. EEG data plays a big role not only in medicine. The EEG data can be used as control commands of an external device, e.g. wheelchair, prosthesis, and many others. To do this, we need to establish models which can correctly classify captured EEG data. This paper presents a model based on Butterworth IIR filter, Fast Fourier transform (FFT), Singular Value Decomposition (SVD) and Decision Tree (DT) as a classifier. It can classify finger flexions with accuracy up to 92.241% for three fingers – thumb, index, and middle.
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Acknowledgment
This work was supported by the Czech Science Foundation under the grant no. GJ16-25694Y and in part by the Grant of SGS No. SP2016/68, VSB-Technical University of Ostrava, Czech Republic.
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Prilepok, M., Jahan, I.S., Snasel, V. (2018). Detection of Finger Flexions Based on Decision Tree. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016. AECIA 2016. Advances in Intelligent Systems and Computing, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-60834-1_7
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