Monitoring Cognitive Workload in Online Videos Learning Through an EEG-Based Brain-Computer Interface

Zhou, Yun; Xu, Tao; Yanping, Cai; Wu, Xiaojun; Dong, Bei

doi:10.1007/978-3-319-58509-3_7

Cited by 14 publications

(4 citation statements)

References 15 publications

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“…Education : Only eight articles were found in this area with two companies. Five articles used Emotiv equipment [ 36 , 37 , 38 , 39 , 40 ] and another three articles used the equipment from InteraXon company [ 41 , 42 , 43 ].…”

Section: Resultsmentioning

confidence: 99%

“…A BCI can analyze a student’s cognitive states, assess the information and visualizations to be used, and change the training method to respond to a student’s individual learning needs [ 38 ]. Furthermore, a BCI can help students refocus and increase their interest and engagement in the learning process [ 40 ]. The neurofeedback system in BCIs can help teachers to better observe and understand students, and it can aid them in customizing their approach based on a student’s needs.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

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Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…A BCI may assess and monitor a learner's cognitive states, gather the relevant information, and then alter the training procedure to meet the specific learning demands of that student [35]. Moreover, students can improve their enthusiasm in the learning process [36].…”

Section: A the Use Of Neurotechnologies And Neurosciencementioning

confidence: 99%

Cognitive and Affective Brain–Computer Interfaces for Improving Learning Strategies and Enhancing Student Capabilities: A Systematic Literature Review

Jamil

Belkacem²,

Ouhbi

et al. 2021

IEEE Access

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Brain-computer interface (BCI) technology has the potential to positively contribute to the educational learning environment, which faces many challenges and shortcomings. Cognitive and affective BCIs can offer a deep understanding of brain mechanisms, which may improve learning strategies and increase brain-based skills. They can offer a better empirical foundation for teaching-learning methodologies, including adjusting learning content based on brain workload, measuring student interest of a topic, or even helping students focus on specific tasks. The latest findings from emerging BCI technology, neuroscience, cognitive sciences, and psychology could be used in learning and teaching strategies to improve student abilities in education. This study investigates and analyzes the research on BCI patterns and its implementation for enhancing cognitive capabilities of students. The results showed that there is insufficient literature on BCI that addresses students with disabilities in the learning process. Further, our analysis revealed a bias toward the significance of cognitive process factors compared with other influential factors, such as the learning environment and emotions that influence learning. Finally, we concluded that BCI technology could improve students' learning and cognitive skills-when consistently associated with the different pedagogical teaching-learning strategies-for better academic achievement.INDEX TERMS 21st century abilities, Applications in subject areas, Brain computer interface, Improving classroom teaching, Neurofeedback, Teaching/learning strategies.

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“…Coyle et al (2015) demonstrated that people in minimally conscious states may be able to operate an Active BCI communication system. Zhou et al (2017) claim that "The cognitive workload that students experience while learning instructional materials determines success in learning" (p64) 33 and apply an Active BCI to identify and classify cognitive load in students watching instructive videos.…”

Section: Speculativementioning

confidence: 99%

Privacy and brain-computer interfaces

Wahlstrom¹,

Fairweather²,

Ashman³

2016

SIGCAS Comput. Soc.

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Brain-Computer Interfaces (BCIs) interpret neural activity, applying it to the control of external devices. As BCIs approach market viability, ethical implications come under consideration. This paper identifies potential privacy disruptions. BCI literature is reviewed in order to identify a BCI typology likely to support a privacy analysis. The typology describes the active, reactive, passive and hybrid types of BCI and, where possible, includes examples that are further classified as existing, prospective or speculative. A review of privacy theory supports an analysis that juxtaposes privacy theory and BCI technologies. The analysis finds that while all four types of BCI have potential for disrupting privacy, disruptions are more likely to arise from the use of reactive, passive and hybrid BCIs. Limitations and directions for future research close the paper.

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Monitoring Cognitive Workload in Online Videos Learning Through an EEG-Based Brain-Computer Interface

Cited by 14 publications

References 15 publications

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Cognitive and Affective Brain–Computer Interfaces for Improving Learning Strategies and Enhancing Student Capabilities: A Systematic Literature Review

Privacy and brain-computer interfaces

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