Predicting Students' Concentration in Cognitive Activities Using EEG and Deep Learning Techniques

Abstract

In an era of social media and online learning platforms, there are several opportunities for learning different technologies and topics that students do not easily understand. However, it also presents challenges by diverting students’ attention, such as notifications, multitasking activities, advertisements, etc. Assessing students' level of focus during cognitive tasks is crucial and complex. This study evaluates students' cognitive engagement through various activities, including arithmetic calculations, reading technical articles, listening to technical podcasts, reading transcripts, browsing the internet, and engaging in relaxation exercises, utilizing EEG signals. Concentration levels are classified using deep learning algorithms, specifically Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Artificial Neural Networks (ANN). The performance of these algorithms is also evaluated based on metrics such as accuracy, F1 Score, precision, and loss.