Analisis Band Power, Relative Power, dan Entropi Sinyal EEG saat Relaksasi dengan Mata Tertutup berdasarkan Brain Region
DOI:
https://doi.org/10.33019/jrfi.v6i01.6895Keywords:
EEG, Band Power, Relative Power, Entropy, Brain-Computer Interface (BCI)Abstract
Electroencephalography (EEG) is a widely used neurophysiological method for monitoring brain activity through scalp electrodes. This study investigates EEG signal characteristics in a resting state with eyes closed, focusing on three quantitative features: band power, relative power, and entropy. The experiment involved five healthy volunteers who were instructed to sit in a relaxed position with eyes closed for five minutes in a quiet, dimly lit room. EEG signals were recorded using an Emotiv EPOC+ device with 14 channels placed according to the international 10–20 system. The recorded signals were processed in MATLAB, including bandpass filtering (1–50 Hz), baseline correction, and artifact rejection. Subsequently, the signals were segmented into two-second epochs for feature extraction. Band power was calculated using the Fast Fourier Transform (FFT) for delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands. Relative power was computed as the ratio of each band’s power to the total power of the signal, while signal entropy was estimated using Shannon entropy to assess complexity. EEG channels were grouped into four brain regions: frontal, temporal, parietal, and occipital. Results show that the occipital region exhibited the highest average band power, consistent with dominant alpha activity during eye closure. Relative power distributions were uniform across subjects and regions. The highest entropy values were observed in the temporal and frontal regions, indicating higher signal complexity in those areas. These findings highlight the effectiveness of combining spectral and nonlinear features to characterize brain activity during rest and provide valuable baselines for future applications in Brain-Computer Interfaces (BCI), stress detection, and neuropsychological mapping.
Downloads
References
[1] D. J. He, B. Liu, Y. Xu, and H. Zhang, “A Review on EEG Signal Acquisition and Processing for Brain-Computer Interface,” IEEE Reviews in Biomedical Engineering, vol. 16, pp. 1–15, 2023.
[2] S. Sanei and J. A. Chambers, EEG Signal Processing, Wiley-Interscience, 2007.
[3] M. Teplan, “Fundamentals of EEG measurement,” Measurement Science Review, vol. 2, no. 2, pp. 1–11, 2002.
[4] H. Klimesch, “EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis,” Brain Research Reviews, vol. 29, no. 2–3, pp. 169–195, 1999.
[5] A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics,” Journal of Neuroscience Methods, vol. 134, no. 1, pp. 9–21, 2004.
[6] B. N. Cahn and J. Polich, “Meditation states and traits: EEG, ERP, and neuroimaging studies,” Psychological Bulletin, vol. 132, no. 2, pp. 180–211, 2006.
[7] P. L. Nunez and R. Srinivasan, Electric Fields of the Brain: The Neurophysics of EEG, 2nd ed., Oxford University Press, 2006.
[8] R. Barry et al., “EEG differences between eyes-closed and eyes-open resting conditions,” Clinical Neurophysiology, vol. 118, no. 12, pp. 2765–2773, 2007.
[9] M. Xie et al., “Entropy measures in EEG signals: A review,” Entropy, vol. 22, no. 4, pp. 1–35, 2020.
[10] G. D. Smith et al., “Multiscale entropy in EEG for detecting mental fatigue,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 4, pp. 1071–1078, 2020.
[11] A. V. Medvedev, “Does the resting state EEG reflect a true resting state of the brain?,” NeuroImage, vol. 59, no. 1, pp. 905–921, 2012.
[12] J. R. Wolpaw et al., "Brain–computer interfaces for communication and control," Clin. Neurophysiol., vol. 113, no. 6, pp. 767–791, 2002.
[13] A. Subasi, "EEG signal classification using wavelet feature extraction and a mixture of expert model," Expert Syst. Appl., vol. 32, no. 4, pp. 1084–1093, 2007.
[14] G. Pfurtscheller and F. H. Lopes da Silva, "Event-related EEG/MEG synchronization and desynchronization: basic principles," Clin. Neurophysiol., vol. 110, no. 11, pp. 1842–1857, 1999.
[15] F. Lotte et al., "A review of classification algorithms for EEG-based brain–computer interfaces," J. Neural Eng., vol. 4, no. 2, pp. R1–R13, 2007.
[16] D. K. Shannon, "A mathematical theory of communication," Bell Syst. Tech. J., vol. 27, pp. 379–423, 1948.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Iqbal Robiyana, Nurizati, Tedi Sumardi, Aditia Ramadhan, Muhammad Agung Suhendra
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
