Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China.
Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, Anhui, China.
Comput Intell Neurosci. 2021 Nov 23;2021:1462369. doi: 10.1155/2021/1462369. eCollection 2021.
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.
To effectively implement the task of action intention understanding EEG signal classification, we proposed a new feature extraction method by improving discriminative spatial patterns.
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.
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.
动作意图理解 EEG 信号分类对于研究人机交互和意图理解机制是不可或缺的。许多关于分类任务的研究通过使用图论度量来提取分类特征，但分类结果通常不理想。
为了有效地实现动作意图理解 EEG 信号分类任务，我们提出了一种通过改进判别空间模式来提取特征的新方法。
整个频带和融合频带都达到了令人满意的分类准确率。与其他作者的动作意图理解 EEG 信号分类方法相比，新方法在某些方面表现更出色。
新的特征提取方法不仅有效地避免了在求解广义特征值问题时的复杂值，而且还实现了相当高的分类准确率。融合不同频带的分类特征是分类任务的一种有效策略。
