Classification of focal and non focal EEG signals using empirical mode decomposition (EMD), phase space reconstruction (PSR) and neural networks

  • Wei ZengEmail author
  • Mengqing Li
  • Chengzhi Yuan
  • Qinghui Wang
  • Fenglin Liu
  • Ying Wang


Electroencephalogram (EEG) signals can be used to identify the human brain in different disease conditions. Nonetheless, it is difficult to detect the subtle and vital differences in EEG simply by visual inspection because of the non-stationary nature of EEG signals. Specifically, in order to find the epileptogenic focus for medical treatment in the case of a partial epilepsy, an intelligent system that can accurately and automatically detect and discriminate focal and non focal groups of EEG signals is required. This will assist clinicians in locating epileptogenic foci before surgery. In this study we propose a novel method for classification between focal and non focal EEG signals based upon empirical mode decomposition (EMD), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) using EMD, and the third and fourth IMFs components are extracted which contain most of the EEG signals’ energy and are considered to be the predominant IMFs. Second, phase space of the two IMFs componets is reconstructed, in which the properties associated with the EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between the focal and non focal groups of EEG signals. Third, neural networks are then used as the classifier with feature vectors as the input to distinguish between focal and non focal EEG signals based on the difference of system dynamics between the two groups. Finally, experiments are carried out on the Bern Barcelona database to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved accuracy on the 50 pairs and 3750 pairs of EEG signals is reported to be \(96\%\) and \(95.37\%\), respectively. Compared with other state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of focal EEG signals in the clinical application.


Electroencephalogram (EEG) Focal and non focal EEG Empirical mode decomposition (EMD) Phase space reconstruction (PSR) Euclidean distance (ED) System dynamics Neural networks 



This work was supported by the National Natural Science Foundation of China (Grant Nos. 61773194, 61304084), by the Natural Science Foundation of Fujian Province of China (Grant No. 2018J01542), by the Program for New Century Excellent Talents in Fujian Province University and by the Training Program of Innovation and Entrepreneurship for Undergraduates (Grant No. 201811312002).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Physics and Mechanical and Electrical EngineeringLongyan UniversityLongyanChina
  2. 2.Department of Mechanical, Industrial and Systems EngineeringUniversity of Rhode IslandKingstonUSA

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