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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.
Highlights
Action intention understanding is that a subject determines the direct goal behind an object’s motor behaviors [1, 2], which lays a solid foundation for some activities, such as social interaction [3] and language learning [4]
It is important to note that we principally address action intention understanding EEG signal classification in this article
We found that feature extraction, feature selection, and frequency band fusion strategies are extremely effective for action intention understanding EEG signal classification
Summary
Action intention understanding is that a subject determines the direct goal behind an object’s motor behaviors [1, 2], which lays a solid foundation for some activities, such as social interaction [3] and language learning [4]. Some of them focused on neuro mechanism analysis [5, 6, 14], while others pay close attention to signal classification [7,8,9,10,11,12, 14]. It is important to note that we principally address action intention understanding EEG signal classification in this article. An ideal classification result is extremely important for enriching user experiences in real life. The brain-computer interface (BCI) [9,10,11,12] highly depends on classification accuracies
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