## 3 Results and Discussion  
### Implementation and EEG Measurements  
An example of the implemented set-up is shown in Figure 5a, which displays a representative example of a player engaging in the _FIFA_ video game using a controller, with the DSI-24 EEG headset securely positioned on their head. This image captures the typical setup of the gaming environment, highlighting how players interact with the game while their brainwave activity is being monitored. The DSI-24 headset is shown in use, illustrating its fit and placement on the player's scalp, crucial for accurate EEG data collection. The ChatGPT-enabled Furhat Robot, characterized by its ability to exhibit various facial expressions, is shown in Figure 5b. The robot, designed for interactive communication, is shown in the process of conversing with a participant. This image emphasizes the robot's advanced features, including its expressive capabilities and integration with the GPT language model, which enables it to provide feedback and guidance based on the EEG data. The results in Figure 6, presents two distinct brain heatmaps, offering a visual comparison of brain activity under different cognitive states. A heatmap of normal brain activity, characterized by a balanced and uniform distribution of neural activity across various regions of the brain is depicted in Figure 6a. This image serves as a baseline representation of brain function in a relaxed state, with no specific areas showing heightened activity. In contrast, Figure 6b illustrates brain activity under cognitive stress, with a noticeable increase in activity in the frontal lobe. This heatmap highlights the intensified neural activity in the frontal regions, indicative of the brain's response to stress. The active frontal lobe is clearly demarcated by warmer colors on the heatmap, signifying higher levels of brain activity in this area. This comparison between the two states visually demonstrates the brain's dynamic response to stress and the significant role of the frontal lobe in managing cognitive load and stress responses. Figure 5: Experimental setup: (**a**) Player interaction in _FIFA_ gaming environment, (**b**) ChatGPT-enabled Furhat robot with expressive capabilities. The results obtained in this study indicate that cognition and emotional responses can be effectively measured using psychophysiological techniques, particularly EEG, as suggested in the research [31]. The utilization of Fp1 and Fp2 electrodes in this context has revealed a correlation between the complexity and difficulty levels of various video games and the increase in gamma band activity, while changes in the alpha band were not significantly evident. This observation confirms that with heightened visual challenges and difficulty in games, there is an associated increase in gamma band activity, providing valuable insights for game designers aiming to create more engaging experiences. In accordance with the findings documented in [32], the importance of the prefrontal lobe, especially the F3 and F4 regions, in controlling consciousness and emotional responses has been reaffirmed. The study also acknowledges the presence of potential artifacts, such as muscle tension, eye movements, and ambient noise in EEG data. To address these issues, the EEG data in this research were decimated to 128 Hz and subjected to filtering processes, including a notch filter to remove power-line noise in the 49 to 51 Hz range and a high-pass filter for frequencies below 0.2 Hz. Consequently, the processing facilitated the extraction of the five band power features across Delta, Theta, Alpha, Beta, and Gamma frequency bands from the EEG data spanning one-second intervals from each channel. In light of the importance of the prefrontal lobe in emotion recognition and cognitive processing, future studies could benefit from an advanced electrode setup focused on this region.