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Jurnal Riset Fisika Indonesia
Published by Universitas Bangka Belitung
ISSN : 27761460     EISSN : 27976513     DOI : https://doi.org/10.33019/jrfi.v1i2
Core Subject : Science,
Astronomy Computer Science & IT Earth & Planetary Sciences Materials Science & Nanotechnology Physics
The Jurnal Riset Fisika Indonesia (JRFI) (e-ISSN: 2797-6513; p-ISSN: 2776-1460) is an open access and peer-reviewed journal, published by Department of Physics - Universitas Bangka Belitung, which is a dissemination medium for research result from scientists, engineers, and practitioners in many fields of physics. JRFI is a biannual journal issued on December and June. The editors welcome submissions of papers describing recent theoretical and experimental research related to: (1) Theoretical articles; (2) Empirical studies; (3) Practice-oriented papers; (4) Case studies; (5) Review of papers, books, and resources. Focus and scope for JRFI as follows: Theoretical physics Computational physics Material physics Geophysics Instrumentation Applied physics
Arjuna Subject : Fisika dan Astronomi - Fisika dan Astronomi (Lain-Lain)
Articles 121 Documents
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Analisis Band Power, Relative Power, dan Entropi Sinyal EEG saat Relaksasi dengan Mata Tertutup berdasarkan Brain Region Robiyana, Iqbal; Nurizati; Sumardi, Tedi; Ramadhan, Aditia; Suhendra, Muhammad Agung
Jurnal Riset Fisika Indonesia Vol 6 No 01: Desember 2025
Publisher : Jurusan Fisika, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jrfi.v6i01.6895

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.

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