## 3 Materials and Methods
### Feature Extraction
By adding a significant dimension to our feature set, this metric becomes particularly relevant when distinguishing irregular patterns in neural activity. By incorporating PFD, our comprehension of complex neural mechanisms is enhanced, thereby bolstering the capability of our machine learning framework to accurately classify classroom EEG recordings according to individual learning styles: \[\text{PFD}=\frac{log(N)}{log(\frac{N}{N+0.4*N_{meta-crossings}})}\] (5) * Higher-Order Spectral Features Higher-order spectral (HOS) features are an essential component in the investigation of EEG signal characteristics, as they offer a more profound understanding of the complex dynamics of neural activity [41; 42]. In contrast to conventional spectral analysis, higher-order spectra provide a more intricate viewpoint through the inclusion of phase couplings and non-linear interactions among distinct frequency components. By revealing concealed intricacies in EEG signals, this class of characteristics proves indispensable for enhancing our comprehension of the neural mechanisms that underlay variations in learning styles within an educational environment. * Bispectrum Magnitude (mAmp): The Bispectrum Magnitude (mAmp) is an essential metric in our feature extraction collection, providing valuable insights into the complex dynamics of EEG signals [43]. By displaying the magnitude of the bispectrum explicitly, this characteristic offers insight into the phase coupling that occurs among various frequency components. Through an examination of the interconnections among these elements, the mAmp function emerges as a pivotal factor in comprehending the non-linear dynamics intrinsic in the neural activity captured during instructional sessions. * Summation of the Bispectrum Logarithmic Amplitudes Summation (H1): This enhances our set of features by encompassing the logarithmic amplitudes of the bispectrum. By acting as a perceptive lens, this characteristic reveals non-linearities that are concealed within the EEG signal. By employing logarithmic transformation, a more intricate viewpoint can be introduced, facilitating the recognition and analysis of intricate patterns that might serve as indicators of unique learning-style attributes. * Summation of the Bispectrum Logarithmic Amplitudes of Diagonal Elements (H2): The bispectrum logarithmic amplitudes of diagonal elements summation (H2) enhances the comprehensiveness of our analysis by placing particular emphasis on the diagonal elements comprising the bispectrum. This functionality furnishes valuable insights into the precise interrelationships among frequency components, thereby facilitating a comprehensive comprehension of the distinctive spectral attributes inherent in the EEG recordings. By augmenting the interpretability of neural dynamics, the H2 feature fortifies the resilience of our machine learning framework. * The 1 st order moment of amplitudes of the spectral waves of diagonal elements of the bispectrum (H3): An analysis of the amplitude distribution within the bispectrum is conducted in the 1 st Order Spectral Moment of Amplitudes of Diagonal Elements of the Bispectrum (H3) [44]. This functionality provides significant insights into the spectral composition of the EEG signal by presenting valuable information regarding its characteristics. H3 enhances our feature set by characterizing the distribution of amplitudes, thereby making a valuable contribution to the comprehensive comprehension of the neural processes documented in classroom EEG recordings. * Entropy features Entropy features are highly influential metrics within the domain of EEG signal analysis, providing a distinctive viewpoint on the intricacy and consistency of neural activity. Entropy, an information theory-derived concept, offers a quantitative quantification of the disorder, uncertainty, and inconsistency present in a signal. In this study, the entropy feature set includes the approximate and sample entropy; these entropies are explained below.