Action Intention Understanding EEG Signal Classification Based on Improved Discriminative Spatial Patterns

Abstract

Objective. Action intention understanding EEG signal classification is indispensable for investigating human-computer interactions and intention understanding mechanisms. Numerous investigations on classification tasks extract classification features by using graph theory metrics; however, the classification results are usually not good. Method. To effectively implement the task of action intention understanding EEG signal classification, we proposed a new feature extraction method by improving discriminative spatial patterns. Results. The whole frequency band and fusion band achieved satisfactory classification accuracies. Compared with other authors’ methods for action intention understanding EEG signal classification, the new method performs more satisfactorily in some aspects. Conclusions. The new feature extraction method not only effectively avoids complex values when solving the generalized eigenvalue problem but also perfectly realizes appreciable classification accuracies. Fusing the classification features of different frequency bands is a useful strategy for the classification task.