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This paper discusses the potential of the EEG signal for implementation of a practical biometric system using 4 or less channels of 2 different types of EEG recordings. Studies have shown that the EEG signal has biometric potential because the signal varies from person to person and is impossible to replicate and steal. Data were collected from 10 male subjects while resting with eyes open and eyes...
This paper looks into the effects of diseased subjects on the recognition rate of an ECG biometric system. A novel technique for feature extraction, linear predictive coding, is implemented along with neural networks for the classifier. Diseased ECG has been shown reduce the recognition rate of the system by only less than 1% and thus the system is robust towards diseased ECG. This allows for the...
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