Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network
Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2021/6655430 |
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| _version_ | 1850175983042166784 |
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| author | Zhizeng Luo Ronghang Jin Hongfei Shi Xianju Lu |
| author_facet | Zhizeng Luo Ronghang Jin Hongfei Shi Xianju Lu |
| author_sort | Zhizeng Luo |
| collection | DOAJ |
| description | Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, the BFN is constructed on the basis of the threshold matrix of the Pearson correlation coefficient of the mu rhythm among the channels. In addition, a weighted BFN is constructed and expressed by the sum of the existing edge weights to characterize the cerebral cortex activation degree in different movement patterns. Then, on the basis of the topological structures of seven mental tasks, three regional networks centered on the C3, C4, and Cz channels are constructed, which are consistent with correspondence between limb movement patterns and cerebral cortex in neurophysiology. Furthermore, the CIR of each regional functional network is calculated to form three-dimensional vectors. Finally, we use the support vector machine to learn a classifier for multiclass MI tasks. Experimental results show a significant improvement and demonstrate the success of the extracted feature CIR in dealing with MI classification. Specifically, the average classification performance reaches 88.67% which is higher than other competing methods, indicating that the extracted CIR is effective for MI classification. |
| format | Article |
| id | doaj-art-bb213ea5509848ba8e7acb170d5d094b |
| institution | OA Journals |
| issn | 2090-5904 1687-5443 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Neural Plasticity |
| spelling | doaj-art-bb213ea5509848ba8e7acb170d5d094b2025-08-20T02:19:21ZengWileyNeural Plasticity2090-59041687-54432021-01-01202110.1155/2021/66554306655430Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional NetworkZhizeng Luo0Ronghang Jin1Hongfei Shi2Xianju Lu3Institute of Intelligent Control and Robotics, Hangzhou Dizanzi University, Hangzhou, ChinaInstitute of Intelligent Control and Robotics, Hangzhou Dizanzi University, Hangzhou, ChinaThe Fourth Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaInstitute of Intelligent Control and Robotics, Hangzhou Dizanzi University, Hangzhou, ChinaFeature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, the BFN is constructed on the basis of the threshold matrix of the Pearson correlation coefficient of the mu rhythm among the channels. In addition, a weighted BFN is constructed and expressed by the sum of the existing edge weights to characterize the cerebral cortex activation degree in different movement patterns. Then, on the basis of the topological structures of seven mental tasks, three regional networks centered on the C3, C4, and Cz channels are constructed, which are consistent with correspondence between limb movement patterns and cerebral cortex in neurophysiology. Furthermore, the CIR of each regional functional network is calculated to form three-dimensional vectors. Finally, we use the support vector machine to learn a classifier for multiclass MI tasks. Experimental results show a significant improvement and demonstrate the success of the extracted feature CIR in dealing with MI classification. Specifically, the average classification performance reaches 88.67% which is higher than other competing methods, indicating that the extracted CIR is effective for MI classification.http://dx.doi.org/10.1155/2021/6655430 |
| spellingShingle | Zhizeng Luo Ronghang Jin Hongfei Shi Xianju Lu Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network Neural Plasticity |
| title | Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network |
| title_full | Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network |
| title_fullStr | Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network |
| title_full_unstemmed | Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network |
| title_short | Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network |
| title_sort | research on recognition of motor imagination based on connectivity features of brain functional network |
| url | http://dx.doi.org/10.1155/2021/6655430 |
| work_keys_str_mv | AT zhizengluo researchonrecognitionofmotorimaginationbasedonconnectivityfeaturesofbrainfunctionalnetwork AT ronghangjin researchonrecognitionofmotorimaginationbasedonconnectivityfeaturesofbrainfunctionalnetwork AT hongfeishi researchonrecognitionofmotorimaginationbasedonconnectivityfeaturesofbrainfunctionalnetwork AT xianjulu researchonrecognitionofmotorimaginationbasedonconnectivityfeaturesofbrainfunctionalnetwork |