Affiliations: [a] School of Statistics and Data Science, Nanjing Audit University, Nanjing, Jiangsu, PR China
| [b] Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu, PR China
| [c]
Nanjing Les Information System Technology Company, Ltd., Nanjing, Jiangsu, PR China
Correspondence:
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Corresponding authors. Xingliang Xiong and Haixian Wang, Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China. E-mail: xiongxingliang66@163.com (Xingliang Xiong) and E-mail: hxwang@seu.edu.cn (Haixian Wang).
Abstract: BACKGROUND:Classification of action intention understanding is extremely important for human computer interaction. Many studies on the action intention understanding classification mainly focus on binary classification, while the classification accuracy is often unsatisfactory, not to mention multi-class classification. METHOD:To complete the multi-class classification task of action intention understanding brain signals effectively, we propose a novel feature extraction procedure based on thresholding graph metric. RESULTS:Both the alpha frequency band and full-band obtained considerable classification accuracies. Compared with other methods, the novel method has better classification accuracy. CONCLUSIONS:Brain activity of action intention understanding is closely related to the alpha band. The new feature extraction procedure is an effective method for the multi-class classification of action intention understanding brain signals.
