Cognition and Education Management Method of Withdrawal Reaction for Students with Internet Addiction Based on EEG Signal Analysis

Shan, Peng; Pei, Jiayin

doi:10.12738/estp.2018.5.122

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Thus, intelligent participants appeared to have a more complex and chaotic EEG structure. [18] CD and KolmEn Cognition 64 EEG channels 6 Resting state EEG was examined.…”

Section: Articlementioning

confidence: 99%

“…Internet addiction in students has been associated with impaired prefrontal cortex function and a diminished ability to make decisions. Shan et al investigated the pathological abnormalities in the prefrontal cortices of students with internet addiction to help people recover from internet addiction more effectively [18]. Analysis of the results of the chaotic features of internet-addicted students and control groups indicated that, compared with typical students, those with internet addiction have higher neuronal activity and chaotic behavior of EEG signals in the prefrontal cortex at rest.…”

Section: Cognitionmentioning

confidence: 99%

See 1 more Smart Citation

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Kargarnovin,

Hernandez,

Farahani

et al. 2023

Brain Sciences

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(1) Background: Chaos, a feature of nonlinear dynamical systems, is well suited for exploring biological time series, such as heart rates, respiratory records, and particularly electroencephalograms. The primary purpose of this article is to review recent studies using chaos theory and nonlinear dynamical methods to analyze human performance in different brain processes. (2) Methods: Several studies have examined chaos theory and related analytical tools for describing brain dynamics. The present study provides an in-depth analysis of the computational methods that have been proposed to uncover brain dynamics. (3) Results: The evidence from 55 articles suggests that cognitive function is more frequently assessed than other brain functions in studies using chaos theory. The most frequently used techniques for analyzing chaos include the correlation dimension and fractal analysis. Approximate, Kolmogorov and sample entropy account for the largest proportion of entropy algorithms in the reviewed studies. (4) Conclusions: This review provides insights into the notion of the brain as a chaotic system and the successful use of nonlinear methods in neuroscience studies. Additional studies of brain dynamics would aid in improving our understanding of human cognitive performance.

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“…Thus, intelligent participants appeared to have a more complex and chaotic EEG structure. [18] CD and KolmEn Cognition 64 EEG channels 6 Resting state EEG was examined.…”

Section: Articlementioning

confidence: 99%

Section: Cognitionmentioning

confidence: 99%

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Kargarnovin,

Hernandez,

Farahani

et al. 2023

Brain Sciences

View full text Add to dashboard Cite

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A Study of Colour Using Mindwave EEG Sensor

Teixeira

Gomes

2020

Lecture Notes in Computer Science

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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.

show abstract

Cognition and Education Management Method of Withdrawal Reaction for Students with Internet Addiction Based on EEG Signal Analysis

Cited by 4 publications

References 17 publications

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

A Study of Colour Using Mindwave EEG Sensor

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

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