References
Chen, L. N. and Wang, D. Z. (2019). Research progress in responsible innovation of brain-computer interface. Journal of Engineering Studies, 11, 390-399.
Debettencoure, M. T., Cohen, J. D., Lee, R. F., Norman, K. A., and Turk-Browne, N. B. (2015). Closed-loop training of attention with real-time brain imaging. Nature Neuroscience, 18, 470-475.
Dommett, E. J., Devonshire, I. M., Plateau, C. R., Westwell, M. S., and Greefield, S. A. (2011). From scientific theory to classroom practice. The Neuroscientist, 17, 382-388.
Donoghue, J. P. (2002). Connecting cortex to machines: recent advances in brain interfaces. Nature Neuroscience, 5, 1085-1088.
Hammond, D. C. (2005). Neurofeedback with anxiety and affective disorders. Child and Adolescent Psychiatric Clinics of North America, 2005, 14, 105-123.
Heinrich, H., Gevensleben, H., and Strehl, U. (2007). Annotation: neurofeedback-train your brain to train behavior. Journal of Child Psychology and Psychiatry, 48, 3-16.
Hu, H., Li, Y. X., Cao, Y. F., Zhao, Q. H., and Lang, Q. E. (2019). The path and experimental research of brain-computer interaction promoting deep learning: attention intervention analysis in the online learning system. Journal of Distance Education, 4, 54-63.
Jiang, L., Zhang, H., Zhang, L., Wu, C., Sun, Q. C., and Li, H. B. (2018). Mapping knowledge domains analysis on evolution of BCI and potential application in educational field: based on journals of SCI and SSCI from 1985 to 2018. Journal of Distance Education, 4, 27-38.
Katona, J. and Kovari, A. (2016). A brain-computer interface project applied in computer engineering. IEEE Transactions on Education, 59, 319-326.
Ke, Q. C. and Wang, P. L. (2019). Research progress on the application of brain-computer interface technology in education. China Educational Technology, 10, 14-22.
Lebedev, M. A. and Nicolelis, M. A. L. (2006). Brain-machine interfaces: past, present and future. Trends in Neurosciences, 29(9), 536-546.
Li, X. Y., Chen, F., Jia, Y. H., and Liu, X. Y. (2020). Signal detection, processing and challenges of non-invasive brain-computer interface technology. Lecture Notes in Electrical Engineering, 586, 60-67.
Mehmood, R. M. and Lee, H. J. (2017). Towards building a computer aided education system for special students using wearable sensor technologies. Sensors, 17, 1-22.
Ning, X. L., Cao, Y. F., and Zhang, Y. (2018). Analysis of ethical issues in the application of brain-computer interface technology. Medicine & Philosophy, 39, 35-38.
Purdy, N. (2018). Neuroscience and education: how best to filter out the neurononsense from our classrooms. Irish Educational Studies, 27, 197-208.
Ren, Y., An, T., and Ling, R. (2019). Brain-computer interface technology education application: status, trends and challenges. Modern Distance Education, 2, 71-78.
Verkijika, S. F. and De-Wet, L. (2015). Using a brain-computer interface (BCI) in reducing math anxiety: evidence from South Africa. Computers and Education, 81, 113-122.
Wang, P. L., Ke, Q. C., and Zhang, J. Q. (2020). Research on application of brain-computer interface in smart classroom. Open Education Research, 26, 72-81.
Wei, N. (2020). Brain computer interface: a non-mainstream line of artificial intelligence education application. China Information Technology Education, 1, 16.
Xu, Z. G., Chen, Q. H., and Zhang, G. W. (2018). A new generation of human-computer interaction: the status, types and educational application of natural user interface: also on the preliminary outlook of brain-computer interface technology. Journal of Distance Education, 4, 39-48.