Resting State EEG-Based Biometrics for Individual Identification Using Convolutional Neural Networks
Refereed conference paper presented and published in conference proceedings

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AbstractBiometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88 %) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms.
All Author(s) ListMa L, Minett JW, Blu T, Wang WSY
Name of Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Start Date of Conference25/08/2015
End Date of Conference29/08/2015
Place of ConferenceMilan
Country/Region of ConferenceItaly
Journal nameConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Pages2848 - 2851
LanguagesEnglish-United Kingdom

Last updated on 2021-19-09 at 00:16