Motor imagery based brain computer interface (MI-BCI) has the advantage of strong independence that can rely on the spontaneous brain activity of the user to operate external devices. However, MI-BCI still has the problem of poor control effect, which requires more effective feature extraction algorithms and classification methods to extract distinctly separable features from electroencephalogram (EEG) signals. This paper proposes a novel framework based on Bispectrum, Entropy and common spatial pattern (BECSP). Here we use three methods of bispectrum in higher order spectra, entropy and CSP to extract MI-EEG signal features, and then select the most contributing features through tree-based feature selection algorithm. By comparing the classification results of SVM, Random Forest, Naive Bayes, LDA, KNN, Xgboost and Adaboost, we finally decide to use the SVM algorithm based on RBF kernel function which obtained the best result among them for classification. The proposed method is applied to the BCI competition IV data set 2a and BCI competition III data set IVa. On data set 2a, the highest accuracy on the evaluation data set reaches 85%. The experiment on data set IVa can also achieve good results. Compared with other algorithms that use the same data set, the performance of our algorithm has also been improved.

基于运动想象的脑机接口（MI-BCI）具有独立性强的优点，可以依靠用户的自发大脑活动来操作外部设备。然而，MI-BCI仍然存在控制效果差的问题，需要更有效的特征提取算法和分类方法从脑电图（EEG）信号中提取出明显可分离的特征。本文提出了一种基于双谱、熵和公共空间模式（BECSP）的新框架。这里我们使用高阶谱中的双谱、熵和CSP三种方法来提取MI-EEG信号特征，然后通过基于树的特征选择算法选择贡献最大的特征。通过比较分类结果SVM、随机森林、朴素贝叶斯、LDA、KNN、Xgboost 和 Adaboost，我们最终决定使用基于 RBF 核函数的 SVM 算法进行分类。所提出的方法应用于 BCI 竞赛 IV 数据集 2a 和 BCI 竞赛 III 数据集 IVa。在数据集 2a 上，评估数据集的最高准确率达到 85%。在数据集IVa上的实验也能取得不错的效果。与使用相同数据集的其他算法相比，我们算法的性能也得到了提升。