Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor Imagery
ObjectiveCollaborative brain–computer interfaces (cBCIs) can make the BCI output more credible by jointly decoding concurrent brain signals from multiple collaborators. Current cBCI systems usually require all collaborators to execute the same mental tasks (common-work strategy). However, it is stil...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-07-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.683784/full |
| _version_ | 1819149234841059328 |
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| author | Bin Gu Minpeng Xu Minpeng Xu Lichao Xu Long Chen Yufeng Ke Kun Wang Jiabei Tang Dong Ming Dong Ming |
| author_facet | Bin Gu Minpeng Xu Minpeng Xu Lichao Xu Long Chen Yufeng Ke Kun Wang Jiabei Tang Dong Ming Dong Ming |
| author_sort | Bin Gu |
| collection | DOAJ |
| description | ObjectiveCollaborative brain–computer interfaces (cBCIs) can make the BCI output more credible by jointly decoding concurrent brain signals from multiple collaborators. Current cBCI systems usually require all collaborators to execute the same mental tasks (common-work strategy). However, it is still unclear whether the system performance will be improved by assigning different tasks to collaborators (division-of-work strategy) while keeping the total tasks unchanged. Therefore, we studied a task allocation scheme of division-of-work and compared the corresponding classification accuracies with common-work strategy’s.ApproachThis study developed an electroencephalograph (EEG)-based cBCI which had six instructions related to six different motor imagery tasks (MI-cBCI), respectively. For the common-work strategy, all five subjects as a group had the same whole instruction set and they were required to conduct the same instruction at a time. For the division-of-work strategy, every subject’s instruction set was a subset of the whole one and different from each other. However, their union set was equal to the whole set. Based on the number of instructions in a subset, we divided the division-of-work strategy into four types, called “2 Tasks” … “5 Tasks.” To verify the effectiveness of these strategies, we employed EEG data collected from 19 subjects who independently performed six types of MI tasks to conduct the pseudo-online classification of MI-cBCI.Main resultsTaking the number of tasks performed by one collaborator as the horizontal axis (two to six), the classification accuracy curve of MI-cBCI was mountain-like. The curve reached its peak at “4 Tasks,” which means each subset contained four instructions. It outperformed the common-work strategy (“6 Tasks”) in classification accuracy (72.29 ± 4.43 vs. 58.53 ± 4.36%).SignificanceThe results demonstrate that our proposed task allocation strategy effectively enhanced the cBCI classification performance and reduced the individual workload. |
| first_indexed | 2024-12-22T13:58:22Z |
| format | Article |
| id | doaj.art-01ad33146bfa42ca897bd43166799e60 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-22T13:58:22Z |
| publishDate | 2021-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-01ad33146bfa42ca897bd43166799e602022-12-21T18:23:28ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-07-011510.3389/fnins.2021.683784683784Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor ImageryBin Gu0Minpeng Xu1Minpeng Xu2Lichao Xu3Long Chen4Yufeng Ke5Kun Wang6Jiabei Tang7Dong Ming8Dong Ming9Neural Engineering & Rehabilitation Laboratory, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaNeural Engineering & Rehabilitation Laboratory, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaNeural Engineering & Rehabilitation Laboratory, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaNeural Engineering & Rehabilitation Laboratory, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaObjectiveCollaborative brain–computer interfaces (cBCIs) can make the BCI output more credible by jointly decoding concurrent brain signals from multiple collaborators. Current cBCI systems usually require all collaborators to execute the same mental tasks (common-work strategy). However, it is still unclear whether the system performance will be improved by assigning different tasks to collaborators (division-of-work strategy) while keeping the total tasks unchanged. Therefore, we studied a task allocation scheme of division-of-work and compared the corresponding classification accuracies with common-work strategy’s.ApproachThis study developed an electroencephalograph (EEG)-based cBCI which had six instructions related to six different motor imagery tasks (MI-cBCI), respectively. For the common-work strategy, all five subjects as a group had the same whole instruction set and they were required to conduct the same instruction at a time. For the division-of-work strategy, every subject’s instruction set was a subset of the whole one and different from each other. However, their union set was equal to the whole set. Based on the number of instructions in a subset, we divided the division-of-work strategy into four types, called “2 Tasks” … “5 Tasks.” To verify the effectiveness of these strategies, we employed EEG data collected from 19 subjects who independently performed six types of MI tasks to conduct the pseudo-online classification of MI-cBCI.Main resultsTaking the number of tasks performed by one collaborator as the horizontal axis (two to six), the classification accuracy curve of MI-cBCI was mountain-like. The curve reached its peak at “4 Tasks,” which means each subset contained four instructions. It outperformed the common-work strategy (“6 Tasks”) in classification accuracy (72.29 ± 4.43 vs. 58.53 ± 4.36%).SignificanceThe results demonstrate that our proposed task allocation strategy effectively enhanced the cBCI classification performance and reduced the individual workload.https://www.frontiersin.org/articles/10.3389/fnins.2021.683784/fullcollaborative brain-computer interfacestask allocationdivision-of-workcommon-workmotor imagery |
| spellingShingle | Bin Gu Minpeng Xu Minpeng Xu Lichao Xu Long Chen Yufeng Ke Kun Wang Jiabei Tang Dong Ming Dong Ming Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor Imagery Frontiers in Neuroscience collaborative brain-computer interfaces task allocation division-of-work common-work motor imagery |
| title | Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor Imagery |
| title_full | Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor Imagery |
| title_fullStr | Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor Imagery |
| title_full_unstemmed | Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor Imagery |
| title_short | Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor Imagery |
| title_sort | optimization of task allocation for collaborative brain computer interface based on motor imagery |
| topic | collaborative brain-computer interfaces task allocation division-of-work common-work motor imagery |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2021.683784/full |
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