Abstract
Physically active breaks (AB) are currently being proposed as an interesting tool to improve students’ attention. Reviews and meta-analyses confirm their effect on attention, but also warned about the sparse evidence based on vigilance and university students. Therefore, this pilot study aimed to (a) determine the effects of AB in comparison with passive breaks on university students’ vigilance and (b) to validate an analysis model based on machine learning algorithms in conjunction with a multiparametric model based on electroencephalography (EEG) signal features. Through a counterbalanced within-subject experimental study, six university students (two female; mean age = 25.67, STD = 3.61) had their vigilance performances (i.e., response time in Psycho-Motor Vigilance Task) and EEG measured, before and after a lecture with an AB and another lecture with a passive break. A multiparametric model based on the spectral power, signal entropy and response time has been developed. Furthermore, this model, together with different machine learning algorithms, shows that for the taken signals there are significant differences after the AB lesson, implying an improvement in attention. These differences are most noticeable with the SVM with RBF kernel and ANNs with F1-score of 85% and 88%, respectively. In conclusion, results showed that students performed better on vigilance after the lecture with AB. Although limited, the evidence found could help researchers to be more accurate in their EEG analyses and lecturers and teachers to improve their students’ attentions in a proper way.
Highlights
IntroductionVigilance and (b) to validate an analysis model based on machine learning algorithms in conjunction with a multiparametric model based on electroencephalography (EEG) signal features
The same occurs with the alpha band, but in this case there is a transition towards the right lateral and frontal zones while a new area appears in C4
An increase in spectral power is observed that oscillates from the frontal (FC1, FZ, Fc2, FC8) and frontal–parietal zone with a very high and very localised power spectral density (PSD) in pre-C1 to a more diffuse frontal zone (FC1, AF4, FPz, FP2 FZ, Fc2, FC8), with less activity in the post-C1 section
Summary
Vigilance and (b) to validate an analysis model based on machine learning algorithms in conjunction with a multiparametric model based on electroencephalography (EEG) signal features. A multiparametric model based on the spectral power, signal entropy and response time has been developed. This model, together with different machine learning algorithms, shows that for the taken signals there are significant differences after the AB lesson, implying an improvement in attention. These differences are most noticeable with the SVM with.
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