PDF(Pubmed)
Abstract：
UNASSIGNED: Channel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems.
UNASSIGNED: In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA.
UNASSIGNED: The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA.
UNASSIGNED: The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.
UNASSIGNED: In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA.
UNASSIGNED: The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA.
UNASSIGNED: The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.
摘要：
■通道选择已成为影响非侵入性脑机接口系统在现实世界中的广泛应用的关键问题。然而,构建合适的多目标问题模型以及有效的搜索策略是影响多目标信道选择算法性能的关键因素。本文提出了一种两阶段稀疏多目标进化算法（TS-MOEA），以解决脑机接口系统中的通道选择问题。
■在TS-MOEA中，一个两阶段的框架，包括早期和晚期，是为了防止算法停滞。此外，这两个阶段集中在不同的多目标问题模型上，从而平衡TS-MOEA中的趋同和种群多样性。受通道相关矩阵稀疏性的启发，稀疏初始化运算符，它对决策变量使用基于领域知识的分数分配策略，被引入以生成初始种群。此外，利用基于分数的变异算子来提高TS-MOEA的搜索效率。
■使用基于62通道EEG的脑机接口系统评估了TS-MOEA和其他五种最先进的多目标算法的性能，用于疲劳检测任务，结果证明了TS-MOEA的有效性。
■提出的两阶段框架可以帮助TS-MOEA摆脱停滞，并促进多样性和收敛性之间的平衡。综合信道相关矩阵的稀疏性和问题域知识可以有效降低TS-MOEA的计算复杂度，同时提高其优化效率。
■在TS-MOEA中，一个两阶段的框架，包括早期和晚期，是为了防止算法停滞。此外，这两个阶段集中在不同的多目标问题模型上，从而平衡TS-MOEA中的趋同和种群多样性。受通道相关矩阵稀疏性的启发，稀疏初始化运算符，它对决策变量使用基于领域知识的分数分配策略，被引入以生成初始种群。此外，利用基于分数的变异算子来提高TS-MOEA的搜索效率。
■使用基于62通道EEG的脑机接口系统评估了TS-MOEA和其他五种最先进的多目标算法的性能，用于疲劳检测任务，结果证明了TS-MOEA的有效性。
■提出的两阶段框架可以帮助TS-MOEA摆脱停滞，并促进多样性和收敛性之间的平衡。综合信道相关矩阵的稀疏性和问题域知识可以有效降低TS-MOEA的计算复杂度，同时提高其优化效率。
