ENERGY FEATURE EXTRACTION AND SVM CLASSIFICATION OF MOTOR IMAGERY-INDUCED ELECTROENCEPHALOGRAMS
| العنوان: | ENERGY FEATURE EXTRACTION AND SVM CLASSIFICATION OF MOTOR IMAGERY-INDUCED ELECTROENCEPHALOGRAMS |
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| المؤلفون: | Jianing Zheng, Jing Zhao, Liyu Huang |
| المصدر: | Journal of Innovative Optical Health Sciences. :1250006 |
| بيانات النشر: | World Scientific Pub Co Pte Lt, 2012. |
| سنة النشر: | 2012 |
| مصطلحات موضوعية: | medicine.diagnostic_test, Computer science, business.industry, Speech recognition, Feature extraction, Biomedical Engineering, Medicine (miscellaneous), Pattern recognition, Electroencephalography, Atomic and Molecular Physics, and Optics, Hilbert–Huang transform, Electronic, Optical and Magnetic Materials, Support vector machine, ComputingMethodologies_PATTERNRECOGNITION, Motor imagery, Kernel (statistics), medicine, Artificial intelligence, business, Energy (signal processing), Brain–computer interface |
| الوصف: | The precise classification for the electroencephalogram (EEG) in different mental tasks in the research on brain–computer interface (BCI) is the key for the design and clinical application of the system. In this paper, a new combination classification algorithm is presented and tested using the EEG data of right and left motor imagery experiments. First, to eliminate the low frequency noise in the original EEGs, the signals were decomposed by empirical mode decomposition (EMD) and then the optimal kernel parameters for support vector machine (SVM) were determined, the energy features of the first three intrinsic mode functions (IMFs) of every signal were extracted and used as input vectors of the employed SVM. The output of the SVM will be classification result for different mental task EEG signals. The study shows that mean identification rate of the proposed algorithm is 95%, which is much better than the present traditional algorithms. |
| تدمد: | 1793-7205 1793-5458 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::99671b46d4168719906ab5e8e4393b5cTest https://doi.org/10.1142/s179354581250006xTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi...........99671b46d4168719906ab5e8e4393b5c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17937205 17935458 |
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