An Effective Entropy-Assisted Mind-Wandering Detection System Using EEG Signals of MM-SART Database.

Mind-wandering (MW), which is usually defined as a lapse of attention has negative effects on our daily life. Therefore, detecting when MW occurs can prevent us from those negative outcomes resulting from MW. In this work, we first collected a multi-modal Sustained Attention to Response Task (MM-SART) database for MW detection. Eighty-two participants' data were collected in our dataset. For each participant, we collected measures of 32-channels electroencephalogram (EEG) signals, photoplethysmography (PPG) signals, galvanic skin response (GSR) signals, eye tracker signals, and several questionnaires for detailed analyses. Then, we propose an effective MW detection system based on the collected EEG signals. To explore the non-linear characteristics of the EEG signals, we utilize entropy-based features. The experimental results show that we can reach 0.712 AUC score by using the random forest (RF) classifier with the leave-one-subject-out cross-validation. Moreover, to lower the overall computational complexity of the MW detection system, we propose correlation importance feature elimination (CIFE) along with AUC-based channel selection. By using two most significant EEG channels, we can reduce the training time of the classifier by 44.16%. By applying CIFE on the feature set, we can further improve the AUC score to 0.725 but with only 14.6% of the selection time compared with the recursive feature elimination (RFE). Finally, we can apply the current work to educational scenarios nowadays, especially in remote learning systems.

When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models.

Mind-wandering has been shown to largely influence our learning efficiency, especially in the digital and distracting era nowadays. Detecting mind-wandering thus becomes imperative in educational scenarios. Here, we used a wearable eye-tracker to record eye movements during the sustained attention to response task. Eye movement analysis with hidden Markov models (EMHMM), which takes both spatial and temporal eye-movement information into account, was used to examine if participants' eye movement patterns can differentiate between the states of focused attention and mind-wandering. Two representative eye movement patterns were discovered through clustering using EMHMM: centralized and distributed patterns. Results showed that participants with the centralized pattern had better performance on detecting targets and rated themselves as more focused than those with the distributed pattern. This study indicates that distinct eye movement patterns are associated with different attentional states (focused attention vs. mind-wandering) and demonstrates a novel approach in using EMHMM to study attention. Moreover, this study provides a potential approach to capture the mind-wandering state in the classroom without interrupting the ongoing learning behavior.

Pulse laser ablation in water fragmentation of amorphous silica in water with optional NaCI spiking.

Amorphous SiO2 powders having two kinds of medium range order (MRO) were subjected to pulse laser ablation in water (PLAL) for X-ray diffraction, transmission electron microscopy and spectroscopy characterization. The powders fragmented down to 10-20 nm in size are mainly hydrogenated amorphous phase retaining Si-2nd O but not Si-2nd Si MRO. There are minor co-existing crystallites of beta-cristobalite, H2Si2O5 as well as additional alpha-tridymite and Na2SiO3 when water is spiked with NaCI for PLAL. Na-signature also caused lower vibration frequency of the hydrogenated silica network. The opal-like hydrogenated phase assemblage of amorphous phase, cristobalite and tridymite thus formed have a minimum band gap at ca. 5 eV for potential optoelectronic and catalytic applications in UV range and shed light on natural occurrence in dynamic settings such as meteorite impact and lightening strike on silicate-enrich crustal rocks.