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

Author

Liu Hailong ; Jue, Wang ; Chongxun, Zheng

Author_Institution

Key Lab. of Biomed. Inf. Eng., Xi´´an Jiaotong Univ., China

fYear

2005

fDate

26-28 May 2005

Firstpage

115

Lastpage

118

Abstract

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%.

Keywords

electroencephalography; medical signal processing; pattern classification; pattern clustering; self-organising feature maps; EEG structure analysis; GHSOM; adaptive artificial neural network model; brain-computer interface; clustering information; data hierarchical structure; electroencephalogram; growing hierarchical self-organizing map; hierarchical architecture; mental task classification; unsupervised method; visual information; Adaptive systems; Artificial neural networks; Biomedical engineering; Brain computer interfaces; Brain modeling; Data mining; Electroencephalography; Laboratories; Mathematical model; Quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Interface and Control, 2005. Proceedings. 2005 First International Conference on

Print_ISBN

0-7803-8902-6

Type

conf

DOI

10.1109/ICNIC.2005.1499856

Filename

1499856