Improved dilation CapsuleNet for motor imagery and mental arithmetic classification based on fNIRS
Purpose This study aimed to improve the accuracy of brain-computer interface (BCI) systems based on motor imagery (MI) and mental arithmetic (MA) by utilizing functional near-infrared spectroscopy (fNIRS) and an improved dilation CapsuleNet (ID-CapsuleNet) model.Methods The study focused on the char...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Brain-Apparatus Communication |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/27706710.2024.2335886 |
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| author | Yu Li Tao Xu Junhua Li Feng Wan Hongtao Wang |
| author_facet | Yu Li Tao Xu Junhua Li Feng Wan Hongtao Wang |
| author_sort | Yu Li |
| collection | DOAJ |
| description | Purpose This study aimed to improve the accuracy of brain-computer interface (BCI) systems based on motor imagery (MI) and mental arithmetic (MA) by utilizing functional near-infrared spectroscopy (fNIRS) and an improved dilation CapsuleNet (ID-CapsuleNet) model.Methods The study focused on the characteristics of fNIRS and employed large-kernel dilation convolution to extract hemodynamic features from fNIRS data. Inspired by CapsuleNet’s success in image classification, an ID-CapsuleNet model was designed, combining large-kernel dilation convolution and CapsuleNet. Four publicly available datasets (A, B, C, and D) were utilized for evaluating the proposed model. Datasets A and B were MA type, while datasets C and D were MI type. Ablation experiments were conducted to assess the usefulness of large-kernel convolution, dynamic routing, and dilation convolution.Results The average accuracies for each dataset were 95.01%, 76.88%, 74.03%, and 80.29% respectively. Cross-subject average accuracies were 88.72%, 75.80%, 75.78%, and 80.34%. Ablation experiments confirmed the importance of large-kernel convolution, dynamic routing, and dilation convolution in the ID-CapsuleNet model.Conclusion The developed ID-CapsuleNet model demonstrated promising potential for enhancing the performance of BCI systems based on MI and MA. The findings contribute to the advancement of BCI technology, offering improved assistive tools for disabled individuals. |
| format | Article |
| id | doaj-art-769ceb174d74437ba8d4e6ff4cac6bc8 |
| institution | DOAJ |
| issn | 2770-6710 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Brain-Apparatus Communication |
| spelling | doaj-art-769ceb174d74437ba8d4e6ff4cac6bc82025-08-20T02:49:39ZengTaylor & Francis GroupBrain-Apparatus Communication2770-67102024-12-013110.1080/27706710.2024.2335886Improved dilation CapsuleNet for motor imagery and mental arithmetic classification based on fNIRSYu Li0Tao Xu1Junhua Li2Feng Wan3Hongtao Wang4Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaFaculty of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaFaculty of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaDepartment of Electrical and Computer Engineering, Faculty of Science and Engineering, University of Macau, Macau, ChinaFaculty of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaPurpose This study aimed to improve the accuracy of brain-computer interface (BCI) systems based on motor imagery (MI) and mental arithmetic (MA) by utilizing functional near-infrared spectroscopy (fNIRS) and an improved dilation CapsuleNet (ID-CapsuleNet) model.Methods The study focused on the characteristics of fNIRS and employed large-kernel dilation convolution to extract hemodynamic features from fNIRS data. Inspired by CapsuleNet’s success in image classification, an ID-CapsuleNet model was designed, combining large-kernel dilation convolution and CapsuleNet. Four publicly available datasets (A, B, C, and D) were utilized for evaluating the proposed model. Datasets A and B were MA type, while datasets C and D were MI type. Ablation experiments were conducted to assess the usefulness of large-kernel convolution, dynamic routing, and dilation convolution.Results The average accuracies for each dataset were 95.01%, 76.88%, 74.03%, and 80.29% respectively. Cross-subject average accuracies were 88.72%, 75.80%, 75.78%, and 80.34%. Ablation experiments confirmed the importance of large-kernel convolution, dynamic routing, and dilation convolution in the ID-CapsuleNet model.Conclusion The developed ID-CapsuleNet model demonstrated promising potential for enhancing the performance of BCI systems based on MI and MA. The findings contribute to the advancement of BCI technology, offering improved assistive tools for disabled individuals.https://www.tandfonline.com/doi/10.1080/27706710.2024.2335886Brain-computer interfacemotor imagerymental arithmeticfNIRSCapsuleNetdilation convolution |
| spellingShingle | Yu Li Tao Xu Junhua Li Feng Wan Hongtao Wang Improved dilation CapsuleNet for motor imagery and mental arithmetic classification based on fNIRS Brain-Apparatus Communication Brain-computer interface motor imagery mental arithmetic fNIRS CapsuleNet dilation convolution |
| title | Improved dilation CapsuleNet for motor imagery and mental arithmetic classification based on fNIRS |
| title_full | Improved dilation CapsuleNet for motor imagery and mental arithmetic classification based on fNIRS |
| title_fullStr | Improved dilation CapsuleNet for motor imagery and mental arithmetic classification based on fNIRS |
| title_full_unstemmed | Improved dilation CapsuleNet for motor imagery and mental arithmetic classification based on fNIRS |
| title_short | Improved dilation CapsuleNet for motor imagery and mental arithmetic classification based on fNIRS |
| title_sort | improved dilation capsulenet for motor imagery and mental arithmetic classification based on fnirs |
| topic | Brain-computer interface motor imagery mental arithmetic fNIRS CapsuleNet dilation convolution |
| url | https://www.tandfonline.com/doi/10.1080/27706710.2024.2335886 |
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