# Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention
## 7 Discussion
Our study also confirms the crucial role of the prefrontal and parietal regions in attentional processes, consistent with findings from previous research. Numerous studies have investigated the neural correlates of attention using EEG-based approaches with varying methodological frameworks and analytical techniques. While these studies have provided valuable insights, their reliance on traditional graph-theoretical and spectral-based methods limits their ability to capture the full complexity of brain connectivity dynamics. In contrast, hypergraph-based approaches offer a richer framework that better represents the multi-dimensional and frequency-specific interactions underlying attentional mechanisms. Early studies on EEG-based attention research [76; 77; 78] examined delta- and alpha-wave amplitudes as indicators of attention. While these studies laid the groundwork for EEG-based attentional analysis, they relied on a univariate spectral approach. Moreover, their interpretations--linking delta and alpha activity primarily to low awareness--may be overly simplistic. [PERSON] et al. [79] explored EEG-based neural markers of attention in elderly participants, correlating frontal EEG features with reaction times. Although their work underscores the potential of single-channel EEG analysis, it overlooks the distributed and interconnected nature of attentional networks. Similarly, [PERSON] et al. [80] computed spectral power ratios (\(\beta/\gamma\), \(\beta/\alpha\), \(\beta/(\alpha+\theta)\)) for attention recognition--an approach that, while common, treats frequency bands as isolated rather than interdependent components of a complex system. In contrast, hypergraph-based analysis offers a more nuanced perspective by revealing interactions between different connectivity measures at specific frequencies. It addresses these limitations by capturing higher-order connectivity patterns across multiple regions and frequencies simultaneously. Machine learning has also been widely applied to EEG-based attention detection. Several studies [81; 82; 83] used various classification techniques (e.g., Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Naive Bayes) to distinguish attentional states. While these methods are powerful for predictive modeling, they often lack interpretability regarding the underlying neural mechanisms. A hypergraph representation provides a more interpretable structure by modeling attention states as overlapping neural communities rather than treating EEG features as independent inputs for classification. Moreover, passive brain-computer interface (BCI) studies, such as those by [PERSON] et al. [84], aimed to classify mental states (e.g., passive attention, disengagement, drowsiness) using EEG. However, most BCI-based attention-monitoring approaches rely on traditional feature extraction methods that fail to capture the full spectrum of interactions within attentional networks. Hypergraphs enhance these approaches by integrating multi-scale and multi-frequency relationships, leading to more robust and generalizable attention-monitoring systems. While prior studies have made significant contributions to EEG-based attention research, they are often constrained by their methodological frameworks, which rely on simplistic spectral analyses, pairwise connectivity measures, and black-box machine learning models. By contrast, hypergraph-based approaches offer a more comprehensive representation of attentional processes, accounting for higher-order interactions, multi-frequency dynamics, and network-wide connectivity patterns. It is essential to highlight that while the various hypergraph representations explored in this study offer similar insights into brain connectivity, each captures distinct aspects of neural dynamics. These differences contribute to a more comprehensive understanding of the mechanisms underlying figurative attention.