Journal of Northeastern University Natural Science ›› 2019, Vol. 40 ›› Issue (10): 1386-1391.DOI: 10.12068/j.issn.1005-3026.2019.10.004

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EEG-Based Feature Recognition of Stereoscopic Video Acceleration

SHEN Li-li1, GENG Xiao-quan1, XU Li-sheng2   

  1. 1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072,China; 2. School of Medicine & Biological Information Engineering, Northeastern University, Shenyang 110169, China.
  • Received:2018-12-27 Revised:2018-12-27 Online:2019-10-15 Published:2019-10-10
  • Contact: SHEN Li-li
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Abstract: A PMEMD-2JSD-CSP model capable of adaptively extracting EEG features was established to clarify the separability of two classes uniform acceleration motion in deep of stereoscopic video. EEG signals were decomposed by the partial noise assisted multivariate empirical mode decomposition(PNA-MEMD)algorithm to obtain the intrinsic mode function(IMF)at each scale. An effective factor based on Jensen-Shannon distance(JSD)was used to select IMF adaptively from two different ranges, and the screening results were combined according to the weights to form a reconstructed signal. At last, the common spatial pattern(CSP)approach was applied to extract spatial features of the reconstructed signals, and the support vector machine(SVM)algorithm was employed to classify the spatial features. The best classification correct rate is 73.16%, which proves the validity of the feature extraction model for two classes of EEG signals.

Key words: EEG, stereoscopic uniform acceleration motion in deep, visual uncomfortable, MEMD(multivariate empirical mode decomposition), CSP(common spatial pattern)

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