Frequency Band and PCA Feature Comparison for EEG Signal Classification

  • I Wayan Pio Pratama Universitas Pendidikan Ganesha
  • Made Windu Antara Kesiman Ganesha University of Education, Computer Science Departement Denpasar, Indonesia
  • I Gede Aris Gunadi Ganesha University of Education, Computer Science Departement Denpasar, Indonesia


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.


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How to Cite
PRATAMA, I Wayan Pio; KESIMAN, Made Windu Antara; GUNADI, I Gede Aris. Frequency Band and PCA Feature Comparison for EEG Signal Classification. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 12, n. 1, p. 1-12, mar. 2021. ISSN 2541-5832. Available at: <>. Date accessed: 12 may 2021. doi: