(searched for: doi:10.1016/j.apergo.2022.103838)
Published: 17 November 2022
Conference: 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS), 2022-11-17 - 2022-11-19, Orlando, United States
Brain-computer interface (BCI) systems can provide an objective evaluation of a learner’s performance based on their brain signals. Having an accurate assessment of the learning process can facilitate the adaptation of the educational setting to improve the effectiveness of learning. The current study aimed at developing BCI classifiers that can accurately predict the learning performance of children by investigating temporal and spectral characteristics of their brain signals. EEG brain activity collected from children while learning a second language was analyzed by extracting two types of features: frequency-domain features using power spectral density (PSD) analysis and time-domain features using detrended fluctuation analysis (DFA). Two machine learning models (i.e., SVM and KNN) were trained with either PSD features or both PSD and DFA features. It was expected that adding a time-domain feature would improve the prediction accuracy of the models. However, the models with only frequency-domain features performed significantly better, suggesting that EEG temporal correlations may not reflect learning processes. Despite this, both approaches yielded high prediction performance, with SVM reaching 84% accuracy in predicting the learning performance of children. These findings highlight the potential of neurotechnology to be employed in learning analytics to refine traditional methods of academic performance measurement and ultimately achieve personalized learning.
Sensors, Volume 22; https://doi.org/10.3390/s22218368
Current training methods show advances in simulation technologies; however, most of them fail to account for changes in the physical or mental state of the trainee. An innovative training method, adaptive to the trainee’s stress levels as measured by grip force, is described and inspected. It is compared with two standard training methods that ignore the trainee’s state, either leaving the task’s level of difficulty constant or increasing it over time. Fifty-two participants, divided into three test groups, performed a psychomotor training task. The performance level of the stress-adaptive group was higher than for both control groups, with a main effect of t = −2.12 (p = 0.039), while the training time was shorter than both control groups, with a main effect of t = 3.27 (p = 0.002). These results indicate that stress-adaptive training has the potential to improve training outcomes. Moreover, these results imply that grip force measurement has practical applications. Future studies may aid in the development of this training method and its outcomes.
Published: 9 October 2022
Conference: 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2022-10-9 - 2022-10-12, Prague, Czech Republic
Brain Computer Interface (BCI) technology offers the possibility to monitor users’ attention and engagement during learning tasks, enabling adaptation of pedagogical strategies for a personalized learning experience. In this paper, we present an EEG-based passive BCI system for real-time evaluation of user engagement during a language learning task. The EEG Engagement Index, which has been previously associated with attention and vigilance, is measured from three frontal electrodes and used in this system as a neural indicator of engagement. To validate our system, we used it in a human-robot interaction (HRI) setting, in which a robot tutor monitored the learner’s brain activity and adapted its tutoring strategy when a lapse in engagement was detected. We discuss the challenges and preliminary results from our pilot study with eight participants.
Virtual Worlds, Volume 1, pp 62-81; https://doi.org/10.3390/virtualworlds1010005
The purpose of the study was to understand how various aspects of virtual reality and extended reality, specifically, environmental displays (e.g., wind, heat, smell, and moisture), audio, and graphics, can be exploited to cause a good startle, or to prevent them. The TreadPort Active Wind Tunnel (TPAWT) was modified to include several haptic environmental displays: heat, wind, olfactory, and mist, resulting in the Multi-Sensory TreadPort Active Wind Tunnel (MS.TPAWT). In total, 120 participants played a VR game that contained three startling situations. Audio and environmental effects were varied in a two-way analysis of variance (ANOVA) study. Muscle activity levels of their orbicularis oculi, sternocleidomastoid, and trapezius were measured using electromyography (EMG). Participants then answered surveys on their perceived levels of startle for each situation. We show that adjusting audio and environmental levels can alter participants physiological and psychological response to the virtual world. Notably, audio is key for eliciting stronger responses and perceptions of the startling experiences, but environmental displays can be used to either amplify those responses or to diminish them. The results also highlight that traditional eye muscle response measurements of startles may not be valid for measuring startle responses to strong environmental displays, suggesting that alternate muscle groups should be used. The study’s implications, in practice, will allow designers to control the participants response by adjusting these settings.