EEG-based measurement system for monitoring student engagement in learning 4.0

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

A micro-bioimpedance meter for monitoring insulin bioavailability in personalized diabetes therapy

Abstract An on-chip transducer, for monitoring noninvasively the insulin bio-availability in real time after administration in clinical diabetology, is proposed. The bioavailability is assessed as insulin decrease in situ after administration by means of local impedance measurement. Inter-and-intra individual reproducibility is enhanced by a personalized model, specific for the subject, identified and validated during each insulin administration. Such a real-time noninvasive bioavailability measurement allows to increase the accuracy of insulin bolus administration, by attenuating drawbacks of glycemic swings significantly. In the first part of this paper, the concept, the architecture, and the operation of the transducer, as well as details about its prototype, are illustrated. Then, the metrological characterization and validation are reported in laboratory, in vitro on eggplants, ex vivo on pig abdominal non-perfused muscle, and in vivo on a human subject, using injection as a reference subcutaneous delivery of insulin. Results of significant intra-individual reproducibility in vitro and ex vivo point out noteworthy scenarios for assessing insulin bioavailability in clinical diabetology

A personalized FEM model for reproducible measurement of anti-inflammatory drugs in transdermal administration to knee

A personalized model of the human knee for enhancing the inter-individual reproducibility of a measurement method for monitoring Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) after transdermal delivery is proposed. The model is based on the solution of Maxwell Equations in the electric-quasi-stationary limit via Finite Element Analysis. The dimensions of the custom geometry are estimated on the basis of knee circumference at the patella, body mass index, and sex of each individual. An optimization algorithm allows to find out the electrical parameters of each subject by experimental impedance spectroscopy data. Muscular tissues were characterized anisotropically, by extracting Cole–Cole equation parameters from experimental data acquired with twofold excitation, both transversal and parallel to tissue fibers. A sensitivity and optimization analysis aiming at reducing computational burden in model customization achieved a worst-case reconstruction error lower than 5%. The personalized knee model and the optimization algorithm were validated in vivo by an experimental campaign on thirty volunteers, 67% healthy and 33% affected by knee osteoarthritis (Kellgren–Lawrence grade ranging in [1,4]), with an average error of 3%

Role of the Motor Cortex in Virtual Reality-Based Neurofeedback for Emotional Self-regulation

A relationship emerged between the progress in performing neurofeedback exercises for emotional regulation and the trend of the μ -band power spectral density from the bipolar channel FC4-CP4. The exploratory study involved 3 subjects. As emerged in previous studies on the analysis of the emotional response in the motor cortex, the significance of the FC4-CP4 channel is confirmed. As already demonstrated for high μ in FCz-CPz, also μ and μ in FC4-CP4 decrease during emotional regulation training within single sessions and between sessions. This is particularly confirmed if the neurofeedback is done with immersive VR compared to the one in 2D

A Systematic Review on Feature Extraction in Electroencephalography-Based Diagnostics and Therapy in Attention Deficit Hyperactivity Disorder

A systematic review on electroencephalographic (EEG)-based feature extraction strategies to diagnosis and therapy of attention deficit hyperactivity disorder (ADHD) in children is presented. The analysis is realized at an executive function level to improve the research of neurocorrelates of heterogeneous disorders such as ADHD. The Quality Assessment Tool for Quantitative Studies (QATQS) and field-weighted citation impact metric (Scopus) were used to assess the methodological rigor of the studies and their impact on the scientific community, respectively. One hundred and one articles, concerning the diagnostics and therapy of ADHD children aged from 8 to 14, were collected. Event-related potential components were mainly exploited for executive functions related to the cluster inhibition, whereas band power spectral density is the most considered EEG feature for executive functions related to the cluster working memory. This review identifies the most used (also by rigorous and relevant articles) EEG signal processing strategies for executive function assessment in ADHD

A customized bioimpedance meter for monitoring insulin bioavailability

In this work, an instrument to measure the bioimpedance is presented for monitoring insulin bioavailability. The instrument is non-invasive, it is built with off-the-shelf components, and it exploits electrodes with a gel film. This hardware, already successfully used in biomedical applications, is now proposed for identification of a personalized insulin model by evaluating in real-time the impedance variation in injection site. A preliminary experimental campaign was conducted on eggplants, in order to evaluate the instrument employability. The final purpose is to integrate the instrument in modern artificial pancreas and to achieve a customized drug therapy

Instrumentation for Motor Imagery-based Brain Computer Interfaces relying on dry electrodes: a functional analysis

The functional analysis of a novel instrumentation for Brain-Computer Interfaces (BCI) is carried out. This consists of a wireless wearable helmet with only 8 dry electrodes. The brain signals to be measured through an electroencephalography are related to the sensorimotor cortex. The final aim is to distinguish between different motor imagery tasks. Furthermore, this analysis also takes into account the discrimination between two executed movements. Features are extracted from the brain signals by means of a Common Spatial Pattern algorithm. Then, two different classifiers are employed to process the brain signals, namely the Random Forest, and the Support Vector Machine with Gaussian kernel. Their performance was compared in terms of classification accuracy and the best accuracy resulted equal to about 80% when distinguishing between left and right imagined movement, classified by means of the Random Forest. The results of this study aim at giving a contribution to the building of wearable BCIs for daily life applications