Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/68015
Title: Resting state EEG-based biometrics for individual identification using convolutional neural networks
Authors: Ma, L
Minett, J
Blu, T
Wang, WSY 
Issue Date: 2015
Source: The 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), Milan, Italy, Aug. 25-29, 2015, p. 2848-2851
Abstract: Biometrics 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.
Keywords: Visual evoked potentials
Biometrics (access control)
Electroencephalography
Feature extraction
Medical signal processing
Neural nets
Signal classification
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-4244-9271-8 (electronic)
978-1-4244-9270-1 (DVD)
ISSN: 1094-687X
EISSN: 1558-4615
DOI: 10.1109/EMBC.2015.7318985
Appears in Collections:Conference Paper

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