<![CDATA[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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