A New Feature Extraction Method for Recognition

2018 ◽  
Vol 6 (6) ◽  
pp. 1386-1393
Author(s):  
J. Anne Wincy ◽  
Y. Jacob Vetha Raj
2012 ◽  
Vol 9 (5) ◽  
pp. 056009 ◽  
Author(s):  
D Vidaurre ◽  
E E Rodríguez ◽  
C Bielza ◽  
P Larrañaga ◽  
P Rudomin

2011 ◽  
Vol 158 (1) ◽  
pp. 75-88 ◽  
Author(s):  
Bernd Ehret ◽  
Konstantin Safenreiter ◽  
Frank Lorenz ◽  
Joachim Biermann

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xingliang Xiong ◽  
Hua Yu ◽  
Haixian Wang ◽  
Jiuchuan Jiang

Objective. Action intention understanding EEG signal classification is indispensable for investigating human-computer interactions and intention understanding mechanisms. Numerous investigations on classification tasks extract classification features by using graph theory metrics; however, the classification results are usually not good. Method. To effectively implement the task of action intention understanding EEG signal classification, we proposed a new feature extraction method by improving discriminative spatial patterns. Results. The whole frequency band and fusion band achieved satisfactory classification accuracies. Compared with other authors’ methods for action intention understanding EEG signal classification, the new method performs more satisfactorily in some aspects. Conclusions. The new feature extraction method not only effectively avoids complex values when solving the generalized eigenvalue problem but also perfectly realizes appreciable classification accuracies. Fusing the classification features of different frequency bands is a useful strategy for the classification task.


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