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EEG Classification of Imaginary Lower Limb Stepping Movements Based on Fuzzy Support Vector Machine with Kernel-Induced Membership Function
- Source :
- International Journal of Fuzzy Systems. 19:566-579
- Publication Year :
- 2016
- Publisher :
- Springer Science and Business Media LLC, 2016.
-
Abstract
- Although various kinds of motor imageries have been used for BCI applications, imaginary lower limb stepping movement has not been studied yet. The purpose of this study is to investigate the possibilities of using electroencephalography (EEG) signal to classify imaginary lower limb stepping movements and to design a robust motor imagery classifier based on support vector machine (SVM). A cue-based experimental paradigm is designed to record nine-channel EEG associated with imaginary left leg stepping (L-stepping) and right leg stepping (R-stepping) movements from eight healthy subjects. Features including band powers (BPs), common spatial pattern (CSP), and a filter-bank CSP (FB-CSP) were extracted from the recorded EEG. Fuzzy SVM (FSVM) is introduced to this study to classify L-stepping and R-stepping imageries. We propose a novel kernel-induced membership function to address the issue of data relative importance assignment. The FSVM with the membership function suggested in the original work of FSVM (Type-I FSVM) and the FSVM with the one we proposed (Type-II FSVM) is compared. Results indicated that the classification accuracies based on BP features are near the chance level (~50 %). Both alpha-band CSP (71.25 %) and FB-CSP (75.63 %) gave acceptable results as a simple k-NN classifier is performed. Results show that both types of FSVM performed better than the conventional SVM. Also, Type-II FSVM outperforms Type-I FSVM, especially when the alpha-CSP feature is employed, where the improvement in error reduction rate is over 15 %. The highest average L-stepping versus R-stepping classification accuracy over the eight subjects is achieved (86.25 % in single-trial analysis) by FB-CSP and FSVM-II. The high classification result suggests the feasibility of using lower limb stepping imagery to develop a BCI that can control devices or might be able to serve as a neurofeedback tool for users who need lower limb stepping imagery training for gait function improvement.
- Subjects :
- Computer science
Computational intelligence
02 engineering and technology
Electroencephalography
Machine learning
computer.software_genre
Theoretical Computer Science
03 medical and health sciences
0302 clinical medicine
Motor imagery
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
medicine
Brain–computer interface
medicine.diagnostic_test
business.industry
Pattern recognition
Support vector machine
Computational Theory and Mathematics
Kernel (image processing)
020201 artificial intelligence & image processing
Artificial intelligence
Neurofeedback
business
computer
030217 neurology & neurosurgery
Software
Membership function
Subjects
Details
- ISSN :
- 21993211 and 15622479
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- International Journal of Fuzzy Systems
- Accession number :
- edsair.doi...........f0f70b48e4f6698dbc810c8ab7b6cb83