Classification of action intention understanding is important for intelligent human-robot interaction research, and feature extraction is one of the key factors. In recent years, many feature extraction methods were proposed for the classification task. Although these methods make some achievements, it is still necessary to design new methods that can complete the classification task more efficiently. Based on three kinds of action intention understanding EEG signals, we first used synchronization likelihood (SL) to construct functional connectivity matrices in multiple frequency bands, then calculated eleven kinds of weighted brain network metrics in the functional connectivity matrices, subsequently adopted a statistical threshold to determine which kind of metric is the most useful, and finally used the metrics that were selected by the threshold as classification features to carry out the action intention understanding classification task. In experimental results, both eight metrics come from delta band and five metrics come from theta band shown their statistical values (p < 0.05), almost each classification accuracy with the single significant metric feature was higher than random level, the classification accuracy with significant metrics fusion was even close to 80%, and all permutation tests of the real classification accuracies with SVM classifier were less than 0.05. The experimental results suggest that the novel feature extraction method is extremely effective for the classification of action intention understanding EEG signals. Meanwhile, the combination of different features and classifiers given in this paper is useful to the classification tasks.

动作意图理解的分类对于智能人机交互研究很重要，特征提取是关键因素之一。近年来，针对分类任务提出了许多特征提取方法。尽管这些方法取得了一些成就，但仍然有必要设计出可以更有效地完成分类任务的新方法。基于三种对动作意图的理解脑电信号，我们首先使用同步似然（SL）来构建多个频段的功能连接矩阵，然后计算功能连接矩阵中的11种加权脑网络指标，然后采用统计阈值确定哪种指标最有用，最后以阈值选择的度量作为分类特征，进行行动意图理解分类任务。在实验结果中，八个指标均来自三角带，五个指标均来自θ带，它们均显示了统计值（p <0.05），几乎每个具有单个有效指标特征的分类准确性都高于随机水平，具有显着指标​​的分类准确性融合甚至接近80％，并且使用SVM分类器对真实分类准确性进行的所有排列检验均小于0.05。实验结果表明，这种新颖的特征提取方法对于理解脑电信号的动作意图分类非常有效。与此同时，