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Brain State Intelligence and Cognitive Health Through EEG Date Modeling

Brain State Intelligence and Cognitive Health Through EEG Date Modeling

Hong Lin, Jonathan Garza, Gregor Schreiber, Minghao Yang, Yunwei Cui
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 16
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799861546|DOI: 10.4018/IJEHMC.2021010104
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MLA

Lin, Hong, et al. "Brain State Intelligence and Cognitive Health Through EEG Date Modeling." IJEHMC vol.12, no.1 2021: pp.46-61. http://doi.org/10.4018/IJEHMC.2021010104

APA

Lin, H., Garza, J., Schreiber, G., Yang, M., & Cui, Y. (2021). Brain State Intelligence and Cognitive Health Through EEG Date Modeling. International Journal of E-Health and Medical Communications (IJEHMC), 12(1), 46-61. http://doi.org/10.4018/IJEHMC.2021010104

Chicago

Lin, Hong, et al. "Brain State Intelligence and Cognitive Health Through EEG Date Modeling," International Journal of E-Health and Medical Communications (IJEHMC) 12, no.1: 46-61. http://doi.org/10.4018/IJEHMC.2021010104

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Abstract

Electroencephalographic data modeling is widely used in developing applications in the areas of healthcare, as well as brain-computer interface. One particular study is to use meditation research to reach out to the high-end applications of EEG data analysis in understanding human brain states and assisting in promoting human healthcare. The analysis of these states could be the initial step in a process to first predict and later allow individuals to control these states. To this end, the authors begin to build a system for dynamic brain state analysis using EEG data. The system allows users to transit EEG data to an online database through mobile devices, interact with the web server through web interface, and get feedback from EEG data analysis programs on real-time bases. The models perform self-adjusting based on the data sets available in the database. Experimental results obtained from various machine-learning algorithms indicate great potential in recognizing user's brain state with high accuracy. This method will be useful in quick-prototyping onsite brain states feedback systems.