A Deep Learning Framework for Real-Time Classification of Visual Learning Styles from Raw EEG Signals

Resumen

This paper focuses on identifying visual learners in real time using brain signals. The signals come from an EEG, which records brain activity. Usually, EEG data is noisy and needs cleaning before use. Traditional methods use this cleaned data to find learning styles. But cleaning takes time, so the models are not useful for real-time use. This issue is solved by using raw EEG data. Deep learning models are developed that do not need manual feature extraction. These models learn directly from raw data. The main purpose is to check if someone is a visual learner. A visual learner learns best with images and videos. The EEG data was collected from 34 healthy people. These people had no prior knowledge of the content shown. Participants were shown animated educational videos. While watching, EEG signals were recorded. Three deep learning models were used: LSTM, LSTM-CNN and LSTM-FCNN. These models are good at understanding time-based data like EEG. Among them, LSTM-CNN worked the best. It gave the highest accuracy. It had good sensitivity, specificity and F1-score. This method does not need any self-reports. It gives fast results. It works with different lengths of EEG data. So, it is useful in classrooms and other real-world settings. It helps teachers understand the best way students learn. This improves both teaching and learning. This work shows that deep learning with raw EEG is powerful. It quickly and accurately identifies visual learners. This changes the future of detecting learning styles.