Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine
In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre...
| Main Authors: | , , , , |
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| Format: | Article |
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MDPI AG
2014-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/7/12784 |
| _version_ | 1828741694153031680 |
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| author | Xiaoou Li Xun Chen Yuning Yan Wenshi Wei Z. Jane Wang |
| author_facet | Xiaoou Li Xun Chen Yuning Yan Wenshi Wei Z. Jane Wang |
| author_sort | Xiaoou Li |
| collection | DOAJ |
| description | In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates. |
| first_indexed | 2024-04-13T01:05:50Z |
| format | Article |
| id | doaj.art-aff58c7f1a064062a916b69a9375318c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T01:05:50Z |
| publishDate | 2014-07-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-aff58c7f1a064062a916b69a9375318c2022-12-22T03:09:22ZengMDPI AGSensors1424-82202014-07-01147127841280210.3390/s140712784s140712784Classification of EEG Signals Using a Multiple Kernel Learning Support Vector MachineXiaoou Li0Xun Chen1Yuning Yan2Wenshi Wei3Z. Jane Wang4Shanghai Medical Instrumentation College, Shanghai 200093, ChinaDepartment of Biomedical Engineering, Hefei University of Technology, Hefei 230009, ChinaDepartment of Neurology, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, ChinaDepartment of Neurology, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, ChinaDepartment of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaIn this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates.http://www.mdpi.com/1424-8220/14/7/12784brain computer interfacemental taskstroke patientsmultiple kernel learningpolynomial kernelradial basis function kernel |
| spellingShingle | Xiaoou Li Xun Chen Yuning Yan Wenshi Wei Z. Jane Wang Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine Sensors brain computer interface mental task stroke patients multiple kernel learning polynomial kernel radial basis function kernel |
| title | Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine |
| title_full | Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine |
| title_fullStr | Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine |
| title_full_unstemmed | Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine |
| title_short | Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine |
| title_sort | classification of eeg signals using a multiple kernel learning support vector machine |
| topic | brain computer interface mental task stroke patients multiple kernel learning polynomial kernel radial basis function kernel |
| url | http://www.mdpi.com/1424-8220/14/7/12784 |
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