RAMST-CNN: A Residual and Multiscale Spatio-Temporal Convolution Neural Network for Personal Identification with EEG
Author:
Affiliation:
1. School of Computer, Hangzhou Dianzi University
2. Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province
3. University of Yamanashi
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Signal Processing
Link
https://www.jstage.jst.go.jp/article/transfun/E104.A/2/E104.A_2020EAP1018/_pdf
Reference27 articles.
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3. [3] A.J. Nevado-Holgado, F. Marten, M.P. Richardson, and J.R. Terry, “Characterising the dynamics of EEG waveforms as the path through parameter space of a neural mass model: Application to epilepsy seizure evolution,” NeuroImage, vol.59, no.3, pp.2374-2392, Feb. 2012. 10.1016/j.neuroimage.2011.08.111
4. [4] S.K. Yeom, H.I. Suk, and S.W. Lee, “Person authentication from neural activity of face-specific visual self-representation,” Pattern Recogn., vol.46, no.4, pp.1159-1169, April 2013. 10.1016/j.patcog.2012.10.023
5. [5] Y. Jian, C. Shu-Shen, H. Hao-Ran, L. Pei-Peng, and Z. Ning, “Dynamic functional connectivity of electroencephalogram in the resting state,” Acta Physica Sinica, vol.64, no.5, p.58701, March 2015.
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