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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I Wayan Pio Pratama
Universitas Pendidikan Ganesha
Computer Science & IT

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Frequency Band and PCA Feature Comparison for EEG Signal Classification I Wayan Pio Pratama; Made Windu Antara Kesiman; I Gede Aris Gunadi
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 1 (2021): Vol. 12, No. 01 April 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i01.p01

Abstract

The frequency band method is popular in signal processing; this method separates EEG signals into five bands of frequency. Besides the frequency band, the recent research show PCA method gives a good result to classify digits number from EEG signal. Even PCA give a good accuracy to classify digit number from EEG signal, but there are no research shows which one yielded better accuracy between PCA and frequency band to classify digit number from EEG signals. This paper presents the comparison between those methods using secondary data from MindBigData (MDB). The result shows that the frequency band and PCA achieve 9% and 12,5% on average accuracy with the EPOC dataset. The paired Wilcoxon test produces a significant difference in accuracy between methods in the digit classification problem. Experiment with Muse dataset provides 31% accuracy with frequency band method and 24,8% with PCA method. The result is competitive compared to other experiments to classify digit numbers from EEG signals. In conclusion, there is no winner between the two methods since no method fits both datasets used in this research.
Co-Authors I Gede Aris Gunadi Made Windu Antara Kesiman