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  • This paper presents results of person identification experiments, based on individual properties of EEG signals. EEG data of each test subject were acquired in two different measurement sessions, with one year time gap between them. Main focus of these experiments was to assess long-term temporal stability of EEG biometrics. Use of EEG data from separate session leads to identification score about 98%. Results for merged EEG sessions reach in average only 78% success rate. Identification algorithm used for these experiments utilizes Frequency-Zooming Auto-Regression modeling and Mahalanobis distance-based classifier, several improvements for this algorithm are proposed in this paper.
  • This paper presents results of person identification experiments, based on individual properties of EEG signals. EEG data of each test subject were acquired in two different measurement sessions, with one year time gap between them. Main focus of these experiments was to assess long-term temporal stability of EEG biometrics. Use of EEG data from separate session leads to identification score about 98%. Results for merged EEG sessions reach in average only 78% success rate. Identification algorithm used for these experiments utilizes Frequency-Zooming Auto-Regression modeling and Mahalanobis distance-based classifier, several improvements for this algorithm are proposed in this paper. (en)
Title
  • EEG signal as biometric characteristic and its long-term temporal stability
  • EEG signal as biometric characteristic and its long-term temporal stability (en)
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  • EEG signal as biometric characteristic and its long-term temporal stability
  • EEG signal as biometric characteristic and its long-term temporal stability (en)
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  • RIV/68407700:21230/12:00193379!RIV13-MSM-21230___
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  • RIV/68407700:21230/12:00193379
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  • electroencephalography; biometrics; person identification; frequency-zooming auto-regression modeling (en)
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  • [61684CE6C83A]
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  • Prague
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  • POSTER 2012 - 16th International Student Conference on Electrical Engineering
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  • Kostílek, Milan
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  • České vysoké učení technické v Praze
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  • 978-80-01-05043-9
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  • 21230
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