Human attention detection system using deep learning and brain–computer interface-Reference-Cited by-同舟云学术

Human attention detection system using deep learning and brain–computer interface

Published:2024-03-28 Issue:18 Volume:36 Page:10927-10940
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Nair S. Anju Latha^ORCID,Megalingam Rajesh Kannan

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00521-024-09628-8.pdf

Reference60 articles.

1. Goldberg P, Sümer Ö, Stürmer K, Wagner W, Göllner R, Gerjets P, Trautwein U (2021) Attentive or not? toward a machine learning approach to assessing students’ visible engagement in classroom instruction. Educ Psychol Rev 33:27–49. https://doi.org/10.1007/s10648-019-09514-z

2. Zaletelj J, Košir A (2017) Predicting students’ attention in the classroom from Kinect facial and body features. EURASIP J Image Video Process 2017:1–12. https://doi.org/10.1186/s13640-017-0228-8

3. Zhang X, Wu CW, Fournier-Viger P, Van LD, Tseng YC (2017) Analysing students' attention in class using wearable devices. In: 2017 IEEE 18th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM), pp 1–9. https://doi.org/10.1109/WoWMoM.2017.7974306

4. Carini RM, Kuh GD, Klein SP (2006) Student engagement and student learning: testing the linkages. Res Higher Educ 47:1–32. https://doi.org/10.1007/s11162-005-8150-9

5. Sezer A, İnel Y, Seçkin AÇ, Uluçınar U (2015) An investigation of university students' attention levels in real classroom settings with NeuroSky's MindWave mobile (EEG) device. In: International Educational Technology Conference–IETC 2015