scholarly journals The Optimal Wavelet Basis Function Selection in Feature Extraction of Motor Imagery Electroencephalogram Based on Wavelet Packet Transformation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 174465-174481 ◽  
Author(s):  
Liwei Cheng ◽  
Duanling Li ◽  
Xiang Li ◽  
Shuyue Yu
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Basis Function ◽  
Wavelet Packet ◽  
Wavelet Basis ◽  
Wavelet Packet Transformation ◽  
Function Selection

Motor Imagery EEG Recognition Based on WPD-CSP and KF-SVM in Brain Computer Interfaces

2014 ◽  
Vol 556-562 ◽  
pp. 2829-2833 ◽  
Author(s):  
Bang Hua Yang ◽  
Ting Wu ◽  
Qian Wang ◽  
Zhi Jun Han
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Kernel Function ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Brain Computer Interfaces ◽  
Recognition Method ◽  
Computer Interfaces

A recognition method based on Wavelet Packet Decomposition - Common Spatial Patterns (WPD-CSP) and Kernel Fisher Support Vector Machine (KF-SVM) is developed and used for EEG recognition in motor imagery brain–computer interfaces (BCIs). The WPD-CSP is used for feature extraction and KF-SVM is used for classification. The presented recognition method includes the following steps: (1) some important EEG channels are selected. The 'haar' wavelet basis is used to take wavelet packet decomposition. And some decomposed sub-bands related with motor imagery for each EEG channel are reconstructed to obtain the relevant frequency information. (2) A six-dimensional feature vector is obtained by the CSP feature extraction to the reconstructed signal. And then the within-class scatter is calculated based on the feature vector. (3) The scatter is added into the radical basis function to construct a new kernel function. The obtained new kernel is integrated into the SVM to act as its kernel function. To evaluate effectiveness of the proposed WPD-CSP + KF-SVM method, the data from the 2008 international BCI competition are processed. A preliminary result shows that the proposed classification algorithm can well recognize EEG data and improve the EEG recognition accuracy in motor imagery BCIs.

Sign in / Sign up

Export Citation Format

Share Document