Real-time and personalized dry EEG neurofeedback increased students’ attention during online teaching in everyday life conditions

Attention tracking in education can serve as a valuable tool for both students and teachers, particularly in the context of online learning. The objectives of our study were to establish the feasibility of developing a non-medical and low-cost dry-EEG neurofeedback system for the general public, usable in everyday life, out of the laboratory, with no need for further expert neurophysiological assistance, in order primarily to monitor and secondarily to optimize the attention levels of university students while watching e-learning videos. A neuromarker for attention was successfully determined using wearable electroencephalography (EEG) in real-life conditions. Assessing the pedagogical benefits of two types of multimodal neurofeedback, either personalized (for each student based on their individual attention level) or aggregate (identical for all students based on a previous cohort’s average attention level), we demonstrated that personalized neurofeedback significantly increased students’ attention levels following an attention drop. On the contrary, the aggregate neurofeedback yielded no positive impact on attention and was perceived as disruptive. This study underlines the feasibility and benefits of providing personalized feedback tailored to individual students during online learning in everyday life conditions, outside of the laboratory. • Feasibility of using wearable dry EEG in real-world educational settings. This study shows that dry EEG systems can be used in everyday conditions, outside of the laboratory, during real-time online teaching, providing a practical, scalable solution for monitoring student attention. • Real-time EEG-based personalized neurofeedback improved attention during online learning. The neurofeedback system, using dry EEG sensors, sent notifications to students when their attention droped during online lectures. Tailored feedback, based on each student’s individual attention levels, led to a stronger positive impact on attention recovery compared to feedback that is based on the group’s average attention levels. • Multimodal feedback system was associated with a good user experience. The neurofeedback notification system incorporated visual (blinking frame), auditory (increased volume), and motor (mouse shake) feedback, which effectively prompted students to regain focus without being overly disruptive to their learning process. • Potential for integration of neurofeedback in online education for improved learning outcomes of students who need it the most. Students having less experience with the topics dealt with in the e-learning videos tended to improve their academic performance thanks to personalized neurofeedback more than more experienced students.