ES1D: A Deep Network for EEG-Based Subject Identification
2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)(2017)
摘要
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.
更多查看译文
关键词
subject identification,EEG,deep learning,convolutional neural networks,1-D CNN
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络