## 1 Introduction  
Within the constantly developing domain of educational research and cognitive neuroscience, the incorporation of cutting-edge technologies has emerged as a critical factor in elucidating the complexities of the human brain's involvement in the learning process. Electroencephalography (EEG) stands out among these technologies as a potent and non-intrusive method for capturing neural activity in real-time [1]. It offers unique insights into the cognitive processes that occur in reaction to diverse stimuli. In the field of education, the integration of EEG recordings into the classroom environment signifies a fundamental change [2, 3], providing an unprecedented opportunity to investigate the complex relationship between varied learning styles and neural patterns. In recent years, several research works have been published on assessing learners' behavior in distance/online learning. Distance/online education has become more important during COVID-19, and plenty of courses in the latest technology have been offered online. Based on the EEG signals collected from 100 individuals, the authors proposed a decision support system to understand learners' attention behavior [4]. Nevertheless, they only studied binary classes (attentive/inattentive) and found that Support Vector Machines (SVMs) predicted attention better than other machine learning algorithms (91.68%). A recent study used 128 channel EEG signals to classify 34 university students' learning styles (learning and memory retrieval) using three types of deep learning models, including Long Short-Term Memory (LSTM), convolutional neural network (CNN), and Fully CNN (LSTM-FCNN). The authors reported a maximum mean accuracy of 94% for LSTM-CNN, which is more efficient than other DL models. In their earlier studies, the same researchers used frequency domain and time-frequency domain analyses and ML classifiers to classify visual and non-visual learners based on their alpha- and gamma-band EEGs [5]. [PERSON] et al. utilized EEG signals features extracted from 14 university students to classify their learning styles based on [PERSON]'s processing dimension theory and achieved a maximum precision of 69.8% with an accuracy of 71.2% utilizing deep convolutional neural network [DCNN] [6]. In the study, [7] analyzed EEG signals to classify the learning styles of learners based on their IQ and stress levels. It was found that alpha-band EEG was more effective in distinguishing learning styles between IQ and stress tests. Innovative methodologies have been made possible by developments in neuroscience research to examine the ways in which the brain facilitates dynamic social interactions in the real world. For instance, academics have initiated investigations into the neurological foundations of social interactions through the comparison of brain activity exhibited by numerous individuals across a range of semi-naturalistic tasks [3;8]. Brain synchrony and comprehension, as well as the predictability of an individual's communicative action, have been correlated in studies involving turn-taking in gestural communication. Significantly, additional research has demonstrated that intricate visual and auditory stimuli (such as natural films) provoke comparable neural activity and affective reactions in observers [9; 10; 11; 12], and that these responses differ significantly according to the attentional engagement of the participants. In addition to demonstrating a correlation between synchrony at the neural and motoric levels, scanning neuroscience research has also demonstrated that the relationship between social factors and brain-to-brain synchrony is moderated by face-to-face interactions [13; 14; 15]. Joint action tasks reveal that increased sentiments of affiliation and social cohesion result from synchronous motor activity among interactive participants [16]; this is especially true in cooperative contexts as opposed to competitive ones, and this is reflected at the neural level.