Brain-Computer Interface Approach for improving the Pedagogical Practices for Virtual Learning: A Conceptual Framework

The coronavirus epidemic (COVID19) has com-pelled the global halting of various services, including educational service, resulting in a massive crisis-response movement of education institutions to online learning platforms. Therefore, teachers had to shift from the traditional face-to-face modality and quickly adapt to virtual learning to continue their education. This conceptual paper discusses a theoretical framework for mon-itoring and improving the level of interaction between students and teachers during virtual learning environments. Through this interaction, teachers can gather some essential cognitive learning behaviors of their students by collecting some biomedical signals. In this conceptual framework, we propose a theoretical end-to-end approach to support teachers in understanding the cognitive learning behaviors of their students during online learning and where face-to-face contact is not possible. This shall be enabled by monitoring the brain patterns of students during their learning, using Brain-computer interface techniques to enhance their cognitive skills and maximize their learning. This approach is also expected to underpin new pedagogical methodologies to support remote learning. © 2022 IEEE.

Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.

The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.

A Perspective of International Collaboration Through Web-Based Telecommunication-Inspired by COVID-19 Crisis.

The tsunami effect of the COVID-19 pandemic is affecting many aspects of scientific activities. Multidisciplinary experimental studies with international collaborators are hindered by the closing of the national borders, logistic issues due to lockdown, quarantine restrictions, and social distancing requirements. The full impact of this crisis on science is not clear yet, but the above-mentioned issues have most certainly restrained academic research activities. Sharing innovative solutions between researchers is in high demand in this situation. The aim of this paper is to share our successful practice of using web-based communication and remote control software for real-time long-distance control of brain stimulation. This solution may guide and encourage researchers to cope with restrictions and has the potential to help expanding international collaborations by lowering travel time and costs.