Classifying Mental Tasks Using Local Mean Decomposition of Electroencephalogram and Support Vector Machine
| Autor: | Liyu Huang, Jie Niu, Jia Ning Zheng, Ying Ju Du |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
medicine.diagnostic_test
business.industry Computer science Speech recognition Pattern recognition General Medicine Electroencephalography Support vector machine Kernel (linear algebra) Task (computing) Kernel (statistics) Decomposition (computer science) medicine Artificial intelligence business Energy (signal processing) |
| Zdroj: | Applied Mechanics and Materials. 330:973-976 |
| ISSN: | 1662-7482 |
| DOI: | 10.4028/www.scientific.net/amm.330.973 |
| Popis: | We present a new combination classification algorithm and test it on the EEG of right and left motor imagery experiment. First, the original EEGs signals are decomposed by Local Mean Decomposition (LMD) and then determine that the first three PFs include the main mental task features. After determining the optimal kernel parameters for support vector machine (SVM), the energy values of the first three PFs of the EEG signals from three electrodes were extracted as the input vectors of SVM. The outputs of SVM were the classification results for different mental task EEG signals. Result shows that mean accuracy of the proposed algorithm is 92.25%, and the best accuracy is 95.00%, which is much better than the present traditional algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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