## 4 Results and Discussion
### Comparison of Helmets
#### 4.3.2 Power Spectral Density in Alpha Waves  
EEG signals, which researchers can measure using various methods, play a crucial role in multiple scientific and clinical applications. For example, researchers have effectively used the evoked potential of the P300 to control robotic devices [35]. In addition, EEG signals are widely used in diagnostic, prognostic, and therapeutic settings [36] and play a key role in human-machine interfaces for emerging technologies, such as soft exoskeletons for rehabilitation [37]. Furthermore, studies have shown that alpha waves correlate with relaxation states [38], and their detection offers promising potential for therapeutic interventions in the treatment of brain disorders [39]. Additionally, the Stockwell Transform, a time-frequency analysis technique, has proven effective in detecting evoked potentials like P300 as well as improving EEG signal processing and classification accuracy in brain-computer interface applications [40]. Various methods assess the functionality of a brain-computer interface system (BCI). In this case, validation occurs exclusively through alpha waves, which are known to increase when the subject reaches a state of relaxation. Comparison Between Ultracortex and Chameleon  
Three healthy subjects participated in the tests, each completing two trials: one using the Ultracortex Mark IV helmet and the other using the Chameleon-1 helmet. Each trial involved maintaining an active state (eyes open) for one minute, which was followed by one minute in a relaxed state (eyes closed). During the trials, the subjects remained seated, motionless, and followed the experiment's instructions. Figure 39 shows the three subjects and the helmets used in the experiments. In the tests, data were collected from the F3 and F4 electrodes at a sampling rate of 250 Hz. Similarly to previous experiments, the EEG signals from the participants were processed using a bandpass filter (0.5-50 Hz), an optional notch filter (50 Hz and 60 Hz), and DC bias removal by common mode rejection (CMR) and high-pass filtering (0.5 Hz) to ensure signal stability. After filtering, the signals from both electrodes were combined to generate a single unified signal. A fourth-order Butterworth filter in the 8 to 13 Hz range was then applied to this signal. Then, a Fourier Transform (FFT) was performed to convert the signal into the frequency domain. Next, the signal's power spectral density (PSD) was calculated for each time interval within the 8 to 13 Hz range. Specifically, the values obtained within this frequency range were averaged at each time point so that each point  
Figure 38: Precise placement of the F3 and F4 electrodes using the Chameleon helmet. displayed the average spectral density for the 8 to 13 Hz frequencies. This process generated a representative value for the band in each interval during the two-minute test period. Each subject had two graphs, one with the commercial helmet and the other with the helmet developed in this paper. The graphs obtained for Subject 1 are shown in Figure 40.  
Figure 40: Alpha PSD (8 to 13 Hz) for Subject 1 (**a**) with the Ultracortex helmet, (**b**) with the Chameleon helmet. Figure 39: Healthy subjects used the commercial and Chameleon helmets in the experiments. Figure 40 shows the values obtained, where the active state produces lower PSD values than the relaxed state. For the Ultracortex headset, the average PSD reached 7.89 \(\upmu\)V\({}^{2}\)/Hz in the active state and 11.45 \(\upmu\)V\({}^{2}\)/Hz in the relaxed state, resulting in a difference of 3.56 \(\upmu\)V\({}^{2}\)/Hz. For the Chameleon headset, the average PSD measured 21.73 \(\upmu\)V\({}^{2}\)/Hz in the active state and 29.61 \(\upmu\)V\({}^{2}\)/Hz in the relaxed state, demonstrating a difference of 7.88 \(\upmu\)V\({}^{2}\)/Hz. These results confirm the expected behavior reported in the literature, showing an increase in PSD during the relaxed state.