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- W4379979504 abstract "As EEG brainwave, which represent human brain activities, are more private, sensitive, and hard to steal or copy, the signals show a lot of promise as a biometric method for user authentication that is much safer. The current study shows a biometric security framework that is based on EEG. In particular, the plan is to lower the noise level with ensemble averaging and a band pass filter, pull out frequency features with fast Fourier transform (FFT), and to use an artificial neural network to do classification. The study followed two different scenarios to show how different authentication applications work. The findings demonstrate that, the average accuracy was about 93.13% when it is used with one person from a group of people. But it has also been revealed there is a classification rate of less than 68.88% is achieved when all subjects are picked out of a large group. The study establishes a strong starting point for future investigation into new biometric approaches considering brainwaves." @default.
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- W4379979504 date "2023-01-01" @default.
- W4379979504 modified "2023-10-14" @default.
- W4379979504 title "Use Multichannel EEG-Based Biometrics Authentication Signal in Real Time Using Neural Network" @default.
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- W4379979504 doi "https://doi.org/10.1007/978-3-031-35442-7_13" @default.
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