For the classification of motor imagery brain-computer interface (BCI) based on electroencephalography (EEG), appropriate features are crucial to obtain a high classification accuracy. Considering the characteristics of the EEG signals, the time-frequency-space three-dimensional features are extracted. Due to a considerable number of the extracted features, the performance of a classifier will degrade. Therefore, it is necessary to implement feature selection. However, existing feature selection methods are easy to fall into a local optimum of a high-dimensional feature selection problem. In this paper, a dimensionality reduction mechanism (called DimReM) is proposed, which gradually reduces the dimension of the search space by removing some unimportant features. In principle, DimReM transforms a high-dimensional feature selection problem into a low-dimensional one. DimReM does not introduce any additional parameters and its implementation is simple. To verify its effectiveness, DimReM is combined with different evolutionary algorithms and different classifiers to select features on various kinds of datasets. Compared with evolutionary algorithms without dimensionality reduction, their augmented versions equipped with DimReM can find feature subsets with higher classification accuracies while smaller numbers of selected features.

对于基于脑电图（EEG）的运动图像脑机接口（BCI）的分类，适当的功能对于获得高分类精度至关重要。考虑到脑电信号的特征，提取了时频空间三维特征。由于提取的特征数量很多，分类器的性能将下降。因此，有必要实现特征选择。但是，现有的特征选择方法容易陷入高维特征选择问题的局部最优。本文提出了一种降维机制（称为DimReM），该机制通过去除一些不重要的特征来逐渐减小搜索空间的大小。原则，DimReM将高维特征选择问题转换为低维特征选择问题。DimReM不引入任何其他参数，并且其实现很简单。为了验证其有效性，将DimReM与不同的进化算法和不同的分类器结合起来，以选择各种数据集上的特征。与没有降维的进化算法相比，配备DimReM的增强版可以找到分类精度更高而所选特征数量更少的特征子集。