Classifying action intention understanding EEG signals based on weighted brain network metric features

Abstract

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.