Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification
المؤلفون المشاركون
Abdullah, Jafri Malin
Abdullah, M. Z.
Lai, Chi Qin
Suandi, Shahrel Azmin
Azman, Azlinda
Ibrahim, Haidi
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-06-02
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Biometric is an important field that enables identification of an individual to access their sensitive information and asset.
In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is able to distinct between two individuals.
The literature reviews have shown that convolutional neural network (CNN) is one of the classification approaches that can avoid the complex stages of preprocessing, feature extraction, and feature selection.
Therefore, CNN is suggested to be one of the efficient classifiers for biometric identification.
Conventionally, input to CNN can be in image or matrix form.
The objective of this paper is to explore the arrangement of EEG for CNN input to investigate the most suitable input arrangement of EEG towards the performance of EEG-based identification.
EEG datasets that are used in this paper are resting state eyes open (REO) and resting state eyes close (REC) EEG.
Six types of data arrangement are compared in this paper.
They are matrix of amplitude versus time, matrix of energy versus time, matrix of amplitude versus time for rearranged channels, image of amplitude versus time, image of energy versus time, and image of amplitude versus time for rearranged channels.
It was found that the matrix of amplitude versus time for each rearranged channels using the combination of REC and REO performed the best for biometric identification, achieving validation accuracy and test accuracy of 83.21% and 79.08%, respectively.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Lai, Chi Qin& Ibrahim, Haidi& Abdullah, M. Z.& Abdullah, Jafri Malin& Suandi, Shahrel Azmin& Azman, Azlinda. 2019. Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1129582
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Lai, Chi Qin…[et al.]. Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1129582
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Lai, Chi Qin& Ibrahim, Haidi& Abdullah, M. Z.& Abdullah, Jafri Malin& Suandi, Shahrel Azmin& Azman, Azlinda. Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1129582
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1129582
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر