Eman Ahmed Abdel Ghaffar
Asst. Professor
Personal identification system based on multidimensional electroencephalographic signals
Indonesian Journal of Electrical Engineering and Computer Science • 2024
Publication Information
Authors
E Abdel ghaffar, M Salama
Keywords
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Journal
Indonesian Journal of Electrical Engineering and Computer Science
Publisher
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Volume
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Issue
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Pages
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publication.type
International
Paper Link
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Supplementary Materials
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Abstract
Personal authentication using electroencephalographic (EEG) signals, is one of
the important applications in brain computer interface (BCI). In this work we
investigate the use of EEG signals as a biometric trait. Multidimensional EEG
signals were represented as symmetric positive-definite (SPD) matrices on a Riemannian manifold. Two experiments are performed in the first; we use minimum
distance to Riemannian mean (MDRM) as a classifier. In the second; SPD matrices are vectorized, and the generated vectors are used to train various machine
learning (ML) classifiers. MDRM classifier achieved a correct recognition rate
(CRR) of 96.92% , while ML classifiers achieved CRR from 95.39% to 99.45%
the important applications in brain computer interface (BCI). In this work we
investigate the use of EEG signals as a biometric trait. Multidimensional EEG
signals were represented as symmetric positive-definite (SPD) matrices on a Riemannian manifold. Two experiments are performed in the first; we use minimum
distance to Riemannian mean (MDRM) as a classifier. In the second; SPD matrices are vectorized, and the generated vectors are used to train various machine
learning (ML) classifiers. MDRM classifier achieved a correct recognition rate
(CRR) of 96.92% , while ML classifiers achieved CRR from 95.39% to 99.45%