## 4 Discussion  
The current study explored differences in the brain activity of patients afflicted with Alzheimer's disease compared to a healthy control cohort, using the quantitative analyses of EEG signals. In particular, two types of analyses were conducted and compared. First, a conventional spectral analysis was conducted to find spectral band power differences between AD subjects and healthy controls. The second approach employed a functional connectivity analysis using phase synchronization across five frequency bands to compare the intra-brain connectivity (global or local) between healthy brains and the ones affected by AD-induced dementia. The results indicated that the spectral analysis did not yield any significant differences between the AD and HC groups, suggesting that it is not an ideal method for the diagnosis of AD based on the EEG. On the other hand, the functional connectivity analysis using the PLV measure showed significant differences between the groups, both in terms of global connectivity and homotopic connectivity. The further analysis of homotopic pairs revealed significantly higher theta-band connectivity localized in the centro-parietal and centro-temporal regions. The dataset used in this study consisted of bipolar derivations, instead of unipolar channel values that are more commonly used in the qEEG analysis [22; 29]. The use of bipolar derivations is seen as a more advantageous method when compared to unipolar or average referencing methods [33], as it can mitigate the issues associated with common active referencing such as volume conduction [34]. Volume conduction, which refers to the leakage of electrical potentials to the neighboring electrodes, can complicate the interpretation of connectivity metrics. Therefore, the use of bipolar derivations in the computation of functional connectivity is highly recommended, as was demonstrated in a recent study in the field of AD detection [30]. The spectral analysis, although being a powerful tool for analyzing specific types of signals, could not confirm any differences between the Alzheimer's patients recruited in this study and their age-matched healthy controls. This is inconsistent with previous reports in which the development of AD was associated with an increase of delta and theta activity as well as a decrease in alpha and beta activity [5; 6; 17]. An explanation for the lack of evidence in the current study could be that the AD subjects included in the sample were only moderately affected by this disorder. In addition, the use of bipolar derivations could \begin{table}
\begin{tabular}{c c c} \hline \hline
**Homotopic Pair** & **Delta Band (1–4 Hz)** & **Theta Band (4–8 Hz)** \\ \hline
**A** & **✗** & **✓** \\
**B** & **✗** & **✗** \\
**C** & **✗** & **✗** \\
**D** & **✗** & **✓** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the results of the post-hoc analysis of the LME regression. have affected the results observed, since past studies made use of unipolar derivations to obtain the spectral powers of the specific EEG channels [3; 5]. Another reason could be due to the number of participants in the selected dataset; had it been higher, different results might have emerged. For example, the study by [PERSON] et al., [5] made use of 75 participants, split into three categories (i.e., 27 with mild AD, 22 with moderate AD, and 27 HCs). While this leaves room for future research to confirm the most suitable computational approach for the detection of severe cases, this study proposes functional connectivity as being a promising tool in detection of early signs of AD from EEG signals [35]. The two connectivity analyses that were subsequently carried out, namely 'Global Connectivity' and 'Homotopic Pair Connectivity', displayed increased communication between brain networks in the AD subject group when compared to the HCs. These findings were first identified in the global connectivity analysis and subsequently confirmed in the homotopic pair connectivity analysis.