Based on Hybrid Fisher and Fuzzy Algorithms to Improve Classification Accuracy of EEG-Based SSVEP Brain Signals 

DU Xiu-lan, ZHANG Jin, MAO Xiao-qian, ZHANG Kai-li, LI Wei

Control Engineering of China ›› 2019, Vol. 26 ›› Issue (6) : 1060-1067.

Control Engineering of China ›› 2019, Vol. 26 ›› Issue (6) : 1060-1067.

Based on Hybrid Fisher and Fuzzy Algorithms to Improve Classification Accuracy of EEG-Based SSVEP Brain Signals 

Author information +
History +

Abstract

 In order to improve the classification accuracy of electroencephalographic based on the steady-state visual evoked potential (SSVEP) in brain computer interface (BCI), a new classification algorithm combining Fisher and Fuzzy is proposed in this paper. First, the algorithm uses Fisher to obtain the optimal projection direction and the threshold value for the EEG signals. Second, calculate the distance d and fuzzy it. Finally, the classification result is obtained by fuzzification and defuzzification process. The classification algorithm overcomes the shortcoming that the samples in the ambiguous area cannot be accurately classified by using a single Fisher classifier in SSVEP for multiple classification problems. In the three, four and five-classification based on the SSVEP, the classification algorithm proposed in this paper has achieved 94.72 %, 92.18 % and 86.08% average classification accuracy that are higher than using a single Fisher classifier achieved 90.07 %, 80.60% and 74.42%. Faced with the low separability data set, the algorithm can significantly improve the classification accuracy.

Key words

Brain-computer interface / steady-state visual evoked potential / Fisher / Fuzzy

Cite this article

Download Citations
DU Xiu-lan, ZHANG Jin, MAO Xiao-qian, ZHANG Kai-li, LI Wei. Based on Hybrid Fisher and Fuzzy Algorithms to Improve Classification Accuracy of EEG-Based SSVEP Brain Signals [J]. Control Engineering of China, 2019, 26(6): 1060-1067

51

Accesses

0

Citation

Detail

Sections
Recommended

/