Liu Tianyu, Ye An
School of Information Engineering, Shanghai Maritime University, Shanghai, China.
Front Neurosci. 2023 Sep 7;17:1251968. doi: 10.3389/fnins.2023.1251968. eCollection 2023.
For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem.
The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space.
The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels.
The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.
对于具有多个脑电图（EEG）通道的非侵入式脑机接口系统（BCI），限制其在现实世界中便捷应用的关键因素是如何在确保任务准确性的同时进行合理的通道选择，这可以建模为一个多目标优化问题。因此，本文针对通道选择问题提出了一个双目标问题模型，并引入了一种领域知识辅助多目标优化算法（DK-MOEA）来解决上述问题。
基于通道连通性矩阵设计了多目标优化问题模型，该模型包含两个目标：一个是任务准确性，另一个能够灵敏地指示BCI中通道的去除状态。所提出的DK-MOEA采用了一个双空间框架，由种群空间和知识空间组成。此外，引入了一个知识辅助更新算子，通过利用存储在知识空间中的领域知识来提高种群空间的搜索效率。
在疲劳检测任务上对所提出的双目标问题模型和DK-MOEA进行了测试，并使用四种先进的多目标进化算法进行比较。实验结果表明，在显著性水平为0.05的情况下，通过威尔科克森秩和检验，所提出的算法在大多数情况下在所有比较算法中取得了最佳结果。还将DK-MOEA与未利用领域知识的版本进行了比较，实验结果验证了知识辅助变异算子的有效性。此外，DK-MOEA与使用所有通道的传统分类算法之间的比较表明，DK-MOEA能够在任务准确性和所选通道数量之间取得平衡。
所制定的双目标优化模型能够在不影响分类准确性的情况下选择最少数量的通道。领域知识的利用提高了DK-MOEA的性能。通过采用所提出的双目标问题模型和DK-MOEA，可以在所选通道数量和疲劳检测任务的准确性之间取得平衡。本文提出的方法可以降低后续数据处理的复杂性，并提高实际应用的便利性。
