Open AccessComplex networks derived from time series and its application in EEG-based emotion assessment with convolutional neural networks
Author(s) -
Zelin Zhang,
Jinyu Xu
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1324/1/012038
Subject(s) - convolutional neural network , computer science , electroencephalography , artificial intelligence , cross entropy , pattern recognition (psychology) , construct (python library) , artificial neural network , time series , recurrent neural network , signal (programming language) , machine learning , psychology , psychiatry , programming language
Construction the complex network paradigm, it is evidenced a new tool for exploring the dynamic mechanism hiding in the time series data which is a trajectory of complex system. This method has been applied in various domains gradually, such as physics, engineering, medicine and economics. In this paper, a new method for network paradigm transforming based on separating with the isoprobability is proposed, then it is applied in EEG signal analysis. The measures of transformed networks from 62-electrods ESI NeuroScan platform were used to construct EEG map. A three-layer convolutional neural network with 15 input channels were built so as to implement EEG-based emotion assessment. By nine fold cross validation, the structure of the convolutional neural network is improved. The simulation shows that our approach is better than differential entropy features based method.