Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from
neurophysiology, computer science, and engineering in an effort to establish real-time …

Most work in the design of learning technology uses click-streams as their primary data
source for modelling & predicting learning behaviour. In this paper we set out to quantify …

Background: This review systematically examined the scientific literature about
electroencephalogram-derived ratio indexes used to assess human mental involvement, in …

Abstract In Machine Learning (ML), a well-known problem is the Dataset Shift problem
where the data in the training and test sets can follow different probability distributions …

Obtaining information about audience engagement in presentations is a valuable asset for
presenters in many domains. Prior literature mostly utilized explicit methods of collecting …

Purpose A research framework that explains adoption intention in students with regard to
brain–computer interface (BCI) games in the learning context was proposed and empirically …

Many factors influence learners' performance on an activity beyond the knowledge required.
Learners' on-task effort has been acknowledged for strongly relating to their educational …

Information about a person's engagement and attention might be a valuable asset in many
settings including work situations, driving, and learning environments. To this end, we …

In human-computer interaction (HCI), there has been a push towards open science, but to
date, this has not happened consistently for HCI research utilizing brain signals due to …

This paper investigates the relation between mental engagement level and sustained
attention in 9 healthy adults performing a Conners'“not-X” continuous performance test …