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Motor Imagery EEG Recognition Based on WPD-CSP and KF-SVM in Brain Computer Interfaces
- Source :
- Applied Mechanics and Materials. :2829-2833
- Publication Year :
- 2014
- Publisher :
- Trans Tech Publications, Ltd., 2014.
-
Abstract
- 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.
- Subjects :
- Computer science
business.industry
Feature vector
Speech recognition
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Basis function
Pattern recognition
General Medicine
Wavelet packet decomposition
Support vector machine
Kernel (linear algebra)
ComputingMethodologies_PATTERNRECOGNITION
Kernel method
Wavelet
Kernel (statistics)
Radial basis function kernel
Artificial intelligence
business
Subjects
Details
- ISSN :
- 16627482
- Database :
- OpenAIRE
- Journal :
- Applied Mechanics and Materials
- Accession number :
- edsair.doi...........4341f2bec647c765407c87fa54f4b4e6