An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine
In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classifica...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/2913019 |
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| author | Xinman Zhang Qi Xiong Yixuan Dai Xuebin Xu Guokun Song |
| author_facet | Xinman Zhang Qi Xiong Yixuan Dai Xuebin Xu Guokun Song |
| author_sort | Xinman Zhang |
| collection | DOAJ |
| description | In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left pinky or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing ECoG signal. |
| format | Article |
| id | doaj-art-d112f6d41a7c4ff98ac7e8bf908533f7 |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-d112f6d41a7c4ff98ac7e8bf908533f72025-08-20T03:19:38ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/29130192913019An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning MachineXinman Zhang0Qi Xiong1Yixuan Dai2Xuebin Xu3Guokun Song4School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, ChinaSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, ChinaSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, ChinaGuangdong Xi’an Jiaotong University Academy, No. 3, Daliangdesheng East Road, Foshan, Guangdong 528000, ChinaSichuan Gas Turbine Research Institute of AVIC, No. 6 Xinjun Road, Xindu District, Chengdu, Sichuan Province, ChinaIn order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left pinky or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing ECoG signal.http://dx.doi.org/10.1155/2020/2913019 |
| spellingShingle | Xinman Zhang Qi Xiong Yixuan Dai Xuebin Xu Guokun Song An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine Complexity |
| title | An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine |
| title_full | An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine |
| title_fullStr | An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine |
| title_full_unstemmed | An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine |
| title_short | An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine |
| title_sort | ecog based binary classification of bci using optimized extreme learning machine |
| url | http://dx.doi.org/10.1155/2020/2913019 |
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