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- W4310502020 abstract "The regular exposure to stress in everyday lives can adversely affect one’s health if its presence is not monitored. Hence, being able to classify the levels of stress an individual experiences remains important to one’s health. This study utilized electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) captured while subjects played a mobile game that test their word construction ability. Multiple trials were conducted at varying difficulties in order to utilize a machine learning model to classify these varying levels of stress. Heart rate as well as galvanic skin response (GSR) data was also collected to reinforce the EEG and fNIRS findings. The data sets were then processed and verified using MATLAB and its machine learning model. The findings show that although each higher level of difficulty did not always increase the amount of stress felt, the overall act of playing the game put individuals at a higher state of stress compared with when they were not playing the game." @default.
- W4310502020 created "2022-12-11" @default.
- W4310502020 creator A5043092851 @default.
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- W4310502020 date "2022-10-26" @default.
- W4310502020 modified "2023-09-29" @default.
- W4310502020 title "Acute Stress Analysis Resulting from Word Construction Using EEG and fNIRS" @default.
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- W4310502020 doi "https://doi.org/10.1109/uemcon54665.2022.9965628" @default.
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