The proposed EEG-based signal processing pipeline using Filter Bank, Common Spatial Pattern, and Support Vector Machine achieved an average accuracy of 76.9% for cognitive and 76.7% for emotional engagement detection.
Observational (n=21)
No
A wearable EEG system using a specific signal processing pipeline can detect cognitive and emotional engagement in students with approximately 77% accuracy, potentially enabling adaptive learning platforms.
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.
Apicella et al. (Thu,) conducted a observational in Healthy volunteers (n=21). EEG-based signal processing pipeline (Filter Bank, Common Spatial Pattern, and Support Vector Machine) vs. Other machine learning classifiers (k-NN, ANN, LDA, DNN, CNN) was evaluated on Average classification accuracy for cognitive engagement (within-subject). The proposed EEG-based signal processing pipeline using Filter Bank, Common Spatial Pattern, and Support Vector Machine achieved an average accuracy of 76.9% for cognitive and 76.7% for emotional engagement detection.