Abstract
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.
Highlights
Brain–computer interface (BCI) systems could use human brain signals for the direct control of external devices (Wang and Jung, 2011; Jiang et al, 2018)
Task Allocation Schemes Based on the Division-of-Work Strategy We propose an optimized task allocation scheme based on the division-of-work strategy for mutual information (MI)-cBCI systems
The map presents clear spectral powers of ERD at α (8–13 Hz) and β (14–28 Hz) bands under various MI tasks. They last until the end of the MI task phase, especially for instruction 3-left hand (LH), 4-right hand (RH), 5-right hand and the left foot (RHLF), and 6-left hand and the right foot (LHRF)
Summary
Collaborative 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). 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. 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 results: Taking the number of tasks performed by one collaborator as the horizontal axis (two to six), the classification accuracy curve of MI-cBCI was mountainlike. 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%).
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