6533b830fe1ef96bd1297944
RESEARCH PRODUCT
ES1D: A Deep Network for EEG-Based Subject Identification
Stamos KatsigiannisDebbie TolsonPablo Arnau-gonzalezMiguel Arevalillo-herrezNaeem Ramzansubject
021110 strategic defence & security studiesmedicine.diagnostic_testbusiness.industryComputer scienceDeep learningFeature extractionSIGNAL (programming language)0211 other engineering and technologiesSpectral densityPattern recognition02 engineering and technologyElectroencephalographyConvolutional neural networkConvolutionIdentification (information)0202 electrical engineering electronic engineering information engineeringmedicine020201 artificial intelligence & image processingArtificial intelligencebusinessdescription
Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the system is evaluated against other traditional classification-based methods that use prior-knowledge-defined features. Results show that the system significantly outperforms other examined approaches, with 94% accuracy at discerning an individual in between a group of 23 different individuals.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-10-01 | 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) |