Feature selection is crucial to develop a brain computer interface (BCI) system which has high classification accuracy and less computational complexity in especially a large feature space. Feature selection (FS) problem has been solved by many various methods. Among these methods, especially evolutionary computation (EC) techniques have gained a lot of attention in recent years. However, there are very few studies in the literature that consider FS problem as a multi-objective problem to find the optimal trade-off between classification accuracy and the number of selected features. Therefore, in this paper, a non-dominated sorting multi-objective symbiotic organism search (NSMOSOS) algorithm is proposed to generate the optimal feature subset in BCI. The efficiency and robustness of the proposed algorithm as a feature selection method is investigated in two datasets based on motor imagery. The highest classification accuracies of NSMOSOS for dataset 1 and dataset 2 are obtained 97.86% with 11 features and 96.57% with average 19 features, respectively. The obtained results demonstrate that the proposed method achieves satisfying results with regard to both the classification accuracy improvement and feature reduction rates for both datasets. The superiority of the proposed method is verified compared with the existing methods for both datasets. Besides, three different versions of symbiotic search organism (SOS) algorithm are improved, and pros and cons of these algorithms are evaluated compared with each other. In conclusion, the paper indicates that the proposed NSMOSOS algorithm is an efficient and practicable technique for FS problem and could be helpful in developing the BCI applications.

特征选择对于开发脑计算机接口（BCI）系统至关重要，该系统在特定的大特征空间中具有较高的分类精度和较低的计算复杂性。特征选择（FS）问题已通过多种方法解决。在这些方法中，尤其是近年来的进化计算（EC）技术引起了很多关注。但是，在文献中很少有研究将FS问题视为多目标问题，以在分类精度和所选特征数量之间找到最佳折衷方案。因此，本文提出了一种非支配的排序多目标共生生物搜索（NSMOSOS）算法来生成BCI中的最优特征子集。在基于运动图像的两个数据集中，研究了该算法作为特征选择方法的效率和鲁棒性。数据集1和数据集2的NSMOSOS的最高分类精度分别为97.86％（11个特征）和96.57％（平均19个特征）。获得的结果表明，该方法在两个数据集的分类精度提高和特征减少率方面均取得令人满意的结果。与两个数据集的现有方法相比，该方法的优越性得到了验证。此外，对三种不同版本的共生搜索有机体（SOS）算法进行了改进，并比较了这些算法的优缺点。结论，