Mental tasks classiflcation and their EEG structures analysis by using the growing hierarchical self-organizing map

被引：0

作者：

Liu, HL ^{[1
]}

Wang, J ^{[1
]}

Zheng, CX ^{[1
]}

机构：

[1] Xian Jiaotong Univ, Key Lab Biomed Informat Engn Educ Minist, Xian 710049, Peoples R China

来源：

2005 First International Conference on Neural Interface and Control Proceedings | 2005年

关键词：

Brain-Computer Interface (BCI); electroencephalogram (EEG); mental tasks classification; GHSOM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The unsupervised method of Growing Hierarchical Self-Organizing Map (GHSOM) was used to perform mental tasks classification. The GHSOM is an adaptive artificial neural network model with hierarchical architecture that is able to detect the hierarchical structure of data. The results indicate that GHSOM provides more detailed clustering information than SOM, and gives visual information about the separability of mental tasks in an intuitive way. The average classification accuracy across 130 task pairs by using GHSOM was up to 96.7%.

引用

页码：115 / 118

页数：4

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