Paper Title:
Energy Entropy Used in Identification Based on Motor Imagery EEG
  Abstract

Subjects are identified by classifying motor imagery EEG signal. Energy entropy was used to preprocess motor imagery EEG data, and the Fisher class separability criterion was applied to extract features. Finally, classification of of extracted features was performed by a Linear discrimination analysis method. Four types motor imagery EEG of three subjects was classified respectively. The results showed that the average classification accuracy achieved over 85%, and the highest was 88.7% on tongue movement imagery EEG

  Info
Periodical
Edited by
Jerry Tian
Pages
274-278
DOI
10.4028/www.scientific.net/AMR.304.274
Citation
X. Dan, "Energy Entropy Used in Identification Based on Motor Imagery EEG", Advanced Materials Research, Vol. 304, pp. 274-278, 2011
Online since
July 2011
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