## 3 Results
### Main Results Obtained
#### 3.2.1 Stress Impact  
Two of the reviewed studies (22.22%) wanted to explore the effect of stress on visuospatial memory performance [32; 33]. The study by [PERSON] et al. [32] employed machine learning (ML) methodology and found that the Random Forest (RF) algorithm obtained the best results when trained and tested under similar conditions, whether stressful or non-stressful. These results were obtained in a visuospatial working memory task where participants had to reproduce a sequence [32]. Another study explored the effect of perceived observation in a virtual environment, which can be considered a stressful condition [33]. The results did not reveal differences between perceived and non-perceived observation conditions on accuracy in two classical visual short-term memory tasks performed through immersive virtual environments, the Benton Visual Retention Test (BVRT)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Objective** & **Main Results** \\ \hline [PERSON] et al., 2021 [32] & Explore ML * to differentiate encoding and retrieval processes of a spatial WM * task & RF * model was best for classifying learning. Frontal regions (dIPFC * and mPFC *) differ between encoding/retrieval phases. \\ \hline [PERSON] et al., 2022 [33] & Examine PO * in visuospatial WM tasks using EEG * & Observation increased task time but did not affect accuracy. Alpha, beta, and theta oscillations change in OC * and dIPFC during visual stimuli presentation and responses. \\ \hline [PERSON] et al., 2023 [34] & Observe EEG during visuospatial memory under different stimuli conditions & Lower memory load and eccentricity showed better performance. CDA * increased with memory load. Lateralized alpha power was observed during retention. \\ \hline [PERSON] and [PERSON], 2024 [35] & Investigate effects of indoor space designs on physiological responses and task performance using EEG & Neutral and personally designed spaces enhanced performance. Environment’s design induced differences in mental stress. \\ \hline [PERSON] et al., 2023 [1] & Explore deep brain activity in spatial memory tasks using iEEG * & MTL * theta oscillations were linked to successful recall and spatial positions. \\ \hline [PERSON] et al., 2022 [36] & Assess cognitive performance under varied background features using EEG & Background features did not affect performance. Theta and beta activity did not correlate with performance. \\ \hline [PERSON] et al., 2018 [37] & Investigate benefits of CL-TMR * during sleep for navigational performance using EEG & CL-TMR group showed performance improvements. Increased fast spindle activity (12-15 Hz) in frontal regions correlated with improved navigation. \\ \hline [PERSON] et al., 2013 [38] & Explore brain activity and memory performance in free spatial navigation using EEG & Performance improved across memory blocks. Low-frequency ranges (2-8 Hz) in PC * correlated with memory. \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline  & Better accuracy was shown for egocentric and low eccentricity tasks. \\ [PERSON] et al., 2021 [39] & Compare active and passive tasks in egocentric and allocentric spatial navigation & Allocentric navigation showed alpha desynchronization in the PC, with beta and gaming power increased in the OC for both strategies, and beta PC activity correlated with other cortical areas during allocentric tasks. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main results of the reviewed studies.  
and the Visual Pattern Test (VPT) [33]. However, participants spent more time completing the BVRT compared to the VPT in the perceived observation condition [31].