## 3 Results  
### Signal Processing  
This section explains the results of the different signal processing stages, including channel summation, noise removal, feature extraction, and classification. In the detection of alpha waves, the sum of the channels located in the occipital region (P7, O1, O2, P8) was performed, as they exhibit similar and synchronized behavior in response to visual stimuli, especially when opening and closing the eyes, which generates a clear and consistent variation across all channels. Similarly, for blink detection, the frontal channels (Fp1 and Fp2) were summed, as they respond consistently and synchronously to these events, producing an evident variation in the signal. Since these channels respond together, amplifying or reducing the signal synchronized, their summation is viable and appropriate. Next, the results of baseline removal in the signal are presented, achieved by subtracting the mean. Figure 8 illustrates this process, comparing the original summed signal with the baseline-corrected signal. As seen in Figure 8a, the signal offsets from the origin of the Y-axis. After applying baseline correction, the signal adjusts towards the origin of the Y-axis without altering the original signal variation. The elimination of baseline noise in the EEG signal is achieved by applying the average subtraction technique, an essential method due to the significant deviation of the original signal from the Y-axis origin. Estimating baseline noise is performed by averaging the signal over one-second intervals. Subsequently, this average is subtracted from the original signal, effectively mitigating noise while retaining the variations pertinent to the analysis. This procedure is iteratively executed every second with a new set of samples, ensuring each interval is free from bias and centered around zero, as explained in Section 2.1. Figure 8 illustrates the effectiveness of the method. Figure 8a shows the original signal with noise, while Figure 8b presents the processed signal. The implemented algorithm effectively removes the noise without compromising the critical information for this study. Figure 7: Interactive physical model of the RelaxQuest game. The signal shown in Figure 8 corresponds to the sum of the electrodes P7, O1, O2, and P8. Similarly, the same procedure was applied to the sum of the electrodes Fp1 and Fp2 to analyze blinking. The summation of signals from multiple electrodes increases baseline noise. However, the proposed method effectively eliminates this noise. Moreover, the baseline average is recalculated every second, enabling the process to adapt to interference from factors such as power grid fluctuations, electrode movements, and other influences. This ensures that the subtracted baseline remains accurate, preserving the integrity of the data. Afterwards, noise reduction is applied using wavelets. In Figure 9, Figure 9 shows the signal without the baseline, while Figure 9 presents the signal with noise reduction. In this figure, the attenuation of the higher frequencies is observed, allowing for greater clarity in the variation in the EEG signal without significantly modifying the signal without baseline. This study uses DWT exclusively for noise reduction, focusing on higher frequencies. This process improves the signal quality by preserving relevant  
Figure 8: Raw EEG signal (**a**) and baseline-corrected signal (**b**). Figure 9: EEG signal without baseline (**a**) and with noise reduction using wavelets (**b**). information and generating a clearer signal, which enables accurate monitoring of changes. Figure 9 compares the signal without noise reduction (Figure 9a) and the attenuated signal (Figure 9b), highlighting the improvement achieved. It is essential to emphasize that the noise reduction implemented through the DWT, primarily focused on higher-frequency components, does not necessarily enhance the detection of alpha wave amplitudes and blinking in this study. This is because the detection process relies on lower-frequency signals.