ArticleScientific reports2022
EEG-based measurement system for monitoring student engagement in learning 4.0.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Wearable Biosensor Technology in Education: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- A vision-based MobileNet-GRU framework for continuous monitoring of student engagement and emotional states.Scientific reports · 2026Article
- Enhancing EEG based cognitive state classification using graph Fourier transform.AIMS neuroscience · 2026Article
- A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel.PloS one · 2026Article
- EEG analysis of brain dynamics in a simulated multi-task and multi-stage learning environment.NPJ science of learning · 2025Article
- Wearable EEG Sensor Analysis for Cognitive Profiling in Educational Contexts.Sensors (Basel, Switzerland) · 2025Article
- A Multimodal Neurophysiological Approach to Evaluate Educational Contents in Terms of Cognitive Processes and Engagement.Bioengineering (Basel, Switzerland) · 2025Article
- EEG-Based Engagement Monitoring in Cognitive Games.Sensors (Basel, Switzerland) · 2025Article
- Facial expression recognition reveals students' engagement in online class: Correlations with six engagement measurements.PloS one · 2025Article
- Generalised machine learning models outperform personalised models for cognitive load classification in real-life settings.Frontiers in digital health · 2025Article
- Wearables for tracking mental state in the classroom: ethical considerations from the literature and high school students.Frontiers in neuroergonomics · 2025Article
- Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis.Sensors (Basel, Switzerland) · 2024Article
- Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst.Frontiers in human neuroscience · 2024Article
- Bridging minds and machines in Industry 5.0: neurobiological approach.Frontiers in human neuroscience · 2024Article
- A review of artificial intelligence methods enabled music-evoked EEG emotion recognition and their applications.Frontiers in neuroscience · 2024Review
- Neurophysiological Evaluation of Students' Experience during Remote and Face-to-Face Lessons: A Case Study at Driving School.Brain sciences · 2023Article
- Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- EEG and peripheral markers of viewer ratings: a study of short films.Frontiers in neuroscience · 2023Article
- Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A wearable system for the personalized EEG-based detection of engagement in learning 4.0 is proposed. In particular, the effectiveness of the proposed solution is assessed by means of the classification accuracy in predicting engagement. The system can be used to make an automated teaching platform adaptable to the user, by managing eventual drops in the cognitive and emotional engagement. The effectiveness of the learning process mainly depends on the engagement level of the learner. In case of distraction, lack of interest or superficial participation, the teaching strategy could be personalized by an automatic modulation of contents and communication strategies. The system is validated by an experimental case study on twenty-one students. The experimental task was to learn how a specific human-machine interface works. Both the cognitive and motor skills of participants were involved. De facto standard stimuli, namely (1) cognitive task (Continuous Performance Test), (2) music background (Music Emotion Recognition-MER database), and (3) social feedback (Hermans and De Houwer database), were employed to guarantee a metrologically founded reference. In within-subject approach, the proposed signal processing pipeline (Filter bank, Common Spatial Pattern, and Support Vector Machine), reaches almost 77% average accuracy, in detecting both cognitive and emotional engagement.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.