Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms

<p dir="ltr">Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based systems achieve...

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Bibliographic Details
Main Author:	Arafat Rahman (8065562) (author)
Other Authors:	Muhammad E. H. Chowdhury (14150526) (author), Amith Khandakar (14151981) (author), Serkan Kiranyaz (3762058) (author), Kh Shahriya Zaman (16891365) (author), Mamun Bin Ibne Reaz (16875933) (author), Mohammad Tariqul Islam (7854059) (author), Maymouna Ezeddin (16891368) (author), Muhammad Abdul Kadir (16869963) (author)
Published:	2021
Subjects:	Biological sciences Bioinformatics and computational biology Engineering Biomedical engineering Information and computing sciences Data management and data science Machine learning Electroencephalography Authentication Biometrics (access control) Feature extraction Headphones Brain modeling Biometric system Electroencephalography (EEG) Keystroke dynamics Identification Multimodal system
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Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms

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