دانلود مقاله انگلیسی رایگان:تجزیه و تحلیل عوامل موثر بر عملکرد سیستم های بیومتریک بر اساس سیگنال های EEG - 2021
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  • Analysis of factors that influence the performance of biometric systems based on EEG signals Analysis of factors that influence the performance of biometric systems based on EEG signals
    Analysis of factors that influence the performance of biometric systems based on EEG signals

    سال انتشار:

    2021


    عنوان انگلیسی مقاله:

    Analysis of factors that influence the performance of biometric systems based on EEG signals


    ترجمه فارسی عنوان مقاله:

    تجزیه و تحلیل عوامل موثر بر عملکرد سیستم های بیومتریک بر اساس سیگنال های EEG


    منبع:

    Sciencedirect - Elsevier - Expert Systems With Applications, 165 (2021) 113967: doi:10:1016/j:eswa:2020:113967


    نویسنده:

    Dustin Carrión-Ojeda


    چکیده انگلیسی:

    Searching for new biometric traits is currently a necessity because traditional ones such as fingerprint, voice, or face are highly prone to forgery. For this reason, the study of bioelectric signals has the potential to develop new biometric systems. A motivation for using electroencephalogram signals is that they are unique to each person and are much more difficult to replicate than conventional biometrics. The objective of this study is to analyze the factors that influence the performance of a biometric system based on electroencephalogram signals. This work uses six different classifiers to compare several decomposition levels of the discrete wavelet transform as a preprocessing technique and also explores the importance of the recording time. These classifiers are Gaussian Naïve Bayes Classifier, K-Nearest Neighbors, Random Forest, AdaBoost, Support Vector Machine, and Multilayer Perceptron. This work proves that the decomposition level does not have a high impact on the overall result of the system. On the other hand, the recording time of electroencephalograms has a significant impact on the performance of the classifiers. It is worth mentioning that this study used two different datasets to validate the results. Finally, our experiments show that Support Vector Machine and AdaBoost are the bestclassifiers for this particular problem since they achieved a sensitivity, specificity, and accuracy of 85.94 ± 1.8, 99.55 ± 0.06, 99.12 ± 0.11 and 95.54 ± 0.53, 99.91 ± 0.01, and 99.83 ± 0.02 respectively.
    Keywords: Biometrics | Electroencephalogram | Discrete Wavelet Transform | Performance factors


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 13
    حجم فایل: 4487 کیلوبایت

    قیمت: رایگان


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