## 3 Materials and Methods
### Feature Extraction  
The objective of the feature extraction procedure was to comprehensively collect a wide array of properties from EEG signals to enable detailed analysis, thereby diving into their complicated character. By incorporating a wide range of feature categories, the proposed methodology sought to precisely represent the complex information that is inherent in the neural data. The selection of each category was conducted with great attention to offer a comprehensive comprehension of the implicit patterns present in the EEG recordings. * Statistical features Within the field of statistical characteristics, this study employed techniques that go beyond simply evaluating components of the EEG signal, delving into the complexities of signal dynamics [37]. The following approach permitted us to determine the statistical characteristics of the signal. * The mean of absolute values of 1 st difference (MAD): This feature provides two important functions: it measures the average of the absolute values of differences between consecutive data points and illuminates the dynamic character of the neural activity by indicating the signal variability: \[\text{MAD1}=\frac{1}{N-1}\sum_{i=1}^{N-1}|x_{i+1}-x_{i}|\] (1)
* The mean of absolute values of 2 nd difference (MAD2): This is a metric that offers a nuanced perspective of the underlying neural processes by determining  
Figure 3: Figure from [PERSON] et al. [20], explaining the setup of the data collection process. Data for the four methods are taken for this study, which includes ‘teacher reads aloud’, ‘video’, teacher lectures’, and ‘group discussion’. The respective session time for each method is also mentioned in the figure. signal smoothness and trend information through the calculation of the average of absolute values of second-order differences: \[\text{MAD}_{2}=\frac{1}{N-2}\sum_{i=1}^{N-2}|x_{i+2}-x_{i}|\] (2) * The utilization of the normalized 1 st difference (NMAD): Normalizing the 1 st difference enhances the interpretability of amplitude fluctuations in EEG signals by one dimension, thereby facilitating a more nuanced comprehension of amplitude changes throughout the recorded epochs: \[\text{NMAD}=\frac{\text{MAD}_{1}}{\text{max}(\text{MAD}_{1})}\] (3) where N represents the total number of EEG samples. ii Fractal dimension features The fractal dimension (FD) characteristics are of the utmost importance in our methodological endeavor to comprehend and describe the intricate dynamics that are intrinsic in EEG signals [38]. The phrase \"fractal dimension\" encompasses the intrinsic irregularities, complexities, and self-replicating qualities that are present in neural activity patterns that encompass multiple dimensions. In this study, we considered Katz and Petrosian FD algorithms, which are commonly used for EEG analysis [39]. * Katz Fractal Dimension (KFD): This is a critical metric utilized in our feature extraction procedure to decipher the complex characteristics of EEG signals. KFD plays a crucial role in the characterization of the EEG waveform's inherent irregularity and complexity [40]. Through the evaluation of the signal's self-similarity at various scales, KFD offers a more intricate understanding of the neural dynamics at play. The significance of this dimensionality measure increases significantly when it comes to capturing the various irregularities and patterns that could potentially signify differences in learning styles within EEG recordings from the classroom: \[\text{KFD}=\frac{log(N)}{log(\frac{d}{L})+log(N)},\text{where d is Euclidean distance}\] (4) * Petrosian Fractal Dimension (PFD): This provides an additional level of understanding regarding the intricacy of the EEG waveform, serving as a complementary component to the KFD. PFD, in its capacity as an indicator of signal irregularity, examines the intricate intricacies of the EEG signal, thereby capturing a broader range of details than conventional measures.