## 4 Discussion  
In this work, mental workload was examined across two different working memory tasks, \(n\)-back, and mental arithmetic. Towards this aim, brain connectivity was estimated employing PLI method. The connections with highest strength are mostly located on parietal-occipital areas as well as in frontal areas. This pattern appears in all task and workload conditions across all frequency bands. Similar findings have been reported in literature. In a large meta-analysis of 85 brain imaging studies utilizing functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) on various types of \(n\)-back tasks, bilateral frontal and parietal regions were found most frequently activated [39]. Regarding the frequency bands related with mental workload, in an EEG study [40] frontal areas were activated in \(\delta\), \(\theta\), \(\alpha\), and \(\beta\) bands, while parietal-occipital areas were activated in \(\theta\), \(\alpha\), and \(\beta\) bands. In an EEG experiment [16], mental workload was found to affect functional connections between Fz and parietal areas in high alpha band, while in [41] frontal theta activity was found to be highly associated with mental workload. Isolation of specific locations and frequency bands is important for efficient real-time performance, for example frontal EEG from a single channel EEG device (Fp1 electrode) was used for mental workload monitoring in four cognitive and motor tasks [42]. Examining the complex brain topological architectures of the PLI network, significant alterations were observed in the clustering coefficient, characteristic path length as well as in small-worldness metrics. Decrements in clustering coefficient indicate that the network is becoming less dense locally, i.e., the neighbor nodes tend to become less connected. Decrements of characteristic path length indicate that remote nodes become more connected. Combining those two observations--since the number of edges is the same in all networks in a specific sparsity level--the conclusion is that the network is rewired, and more edges are present between remote nodes, making more efficient long-range communication. The fact that communication between remote nodes is improved might be the effect of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & \(n\)**-Back** & **Arithmetic Task** \\ \hline SVM & 0.733 (0.099) & 0.671 (0.088) \\ KNN & 0.731 (0.073) & 0.702 (0.091) \\ RF & 0.748 (0.124) & 0.625 (0.090) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy (mean and standard deviation across 10 folds). Figure 4: The confusion matrix for each classifier and task. increased task demands, in the sense that more brain resources are required to perform a more difficult task. Decrement of small-worldness shows that the clustering coefficient has decreased at a larger rate than the characteristic path length. Thus, higher workload has more negative impact on local communication than improvement of global communication. In detail, concerning clustering coefficient, a significant workload effect was apparent for all frequency bands and particularly a converse correlation with cognitive load levels was observed for \(\theta\) and \(\alpha\) frequency bands in the mental arithmetic and in all bands for the \(n\)-back, presenting a less clustered network with increasing task difficulty. Consistent with this observation, other studies also report a reduction in the local density of connections with respect to increased cognitive effort especially for WM task [43; 44] indicating a functional segregation of the brain network. In a similar manner, the small-worldness metric demonstrated significant effects in \(\theta\) and \(\alpha\) bands with regard to mental load in both tasks. On the grounds of this, the small-world network organization indicates the coordination between the dispersed cognitive resources and the level of global network integration in high-demand WM tasks [45].