Attention Patterns Detection Using Brain Computer Interfaces
Proceedings of Annual ACM Southeast Conference (ACMSE 2020)
This publication was published in the Proceedings of the Annual ACM Southeast Conference (ACMSE 2020).
The human brain provides a range of functions such as expressing emotions, controlling the rate of breathing, etc., and its study has attracted the interest of scientists for many years. As machine learning models become more sophisticated, and biometric data becomes more readily available through new non-invasive technologies, it becomes increasingly possible to gain access to interesting biometric data that could revolutionize Human-Computer Interaction. In this research, we propose a method to assess and quantify human attention levels and their effects on learning. In our study, we employ a brain computer interface (BCI) capable of detecting brain wave activity and displaying the corresponding electroencephalograms (EEG). We train recurrent neural networks (RNNS) to identify the type of activity an individual is performing.
Hamza-Lup, Felix G., Aditya Suri, Ionut E. Iacob, Ioana R. Goldbach, Lateef Rasheed, Paul N. Borza.
"Attention Patterns Detection Using Brain Computer Interfaces."
Proceedings of Annual ACM Southeast Conference (ACMSE 2020): 303-204 Tampa, FL.
doi: https://doi.org/10.1145/3374135.3385322 source: https://doi.org/10.1145/3374135.3385322