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A Smart Warning System Design Based on Brainwaves to Maintain Attention

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Conference

2023 ASEE North Central Section Conference

Location

Morgantown, West Virginia

Publication Date

March 24, 2023

Start Date

March 24, 2023

End Date

March 25, 2023

Page Count

8

DOI

10.18260/1-2--44928

Permanent URL

https://strategy.asee.org/44928

Download Count

108

Paper Authors

biography

Haylee Haik Eastern Michigan University, College of Engineering and Technology

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I’m a senior at Eastern Michigan University studying Electrical Engineering. In addition to conducting research, I enjoy leading as president of the Society of Women Engineers organization on campus.

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Jennifer Meng

biography

Qin Hu School of Engineering, Eastern Michigan University Orcid 16x16 orcid.org/0000-0003-0223-8285

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Qin Hu received her B.S. and M.S. degrees in Electrical Engineering from the University of Electronic Science and Technology of China, Chengdu, China, and the Ph.D. degree in Electrical Engineering from Old Dominion University, Norfolk, VA. She is current

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Abstract

The rise of digital technology and increasingly busy lifestyles around the world has surrounded people with a growing number of distractions. These distractions may affect an individual’s ability to retain focus in various areas of life that require concentration, especially learning or driving. Utilizing biotechnology can closely monitor an individual's brain state and provide valuable feedback about how and when to adjust their daily habits to maintain better concentration. This technology can help students regain attention during class periods, leading to better focus on class material, and can help drivers refocus during driving, which can prevent accidents and promote safer driving.

The purpose of this project is to develop a warning system that utilizes data collected from electroencephalography (EEG) technology to evaluate user focus. A 16-channel EEG cap with 19 Ag/AgCl coated electrodes will record brainwave data for a user performing a set of tasks requiring active or passive engagement. The EEG signals will be preprocessed using filters to remove artifacts and confounding events from the data. The data will then be analyzed using Fast Fourier Transform (FFT) to abstract features of the EEG signals associated with active and passive tasks. After these initial calibrations, an external device will be created to alert the user when they enter or exit a focused state. Lastly, a machine learning algorithm will be developed to continuously refine the accuracy of the focus monitoring system for individual users as more data is collected.

Haik, H., & Meng, J., & Hu, Q. (2023, March), A Smart Warning System Design Based on Brainwaves to Maintain Attention Paper presented at 2023 ASEE North Central Section Conference, Morgantown, West Virginia. 10.18260/1-2--44928

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