Application of growing self-organizing map to distinguish between finger tapping and non tapping from brain images

Huang, Pin, Pathirana, Pubudu, Alahakoon, Damminda and Brotchie, Peter 2012, Application of growing self-organizing map to distinguish between finger tapping and non tapping from brain images, in ICIAfS 2012 : Proceedings of the 2012 IEEE 6th International Conference on Information and Automation for Sustainability, IEEE, Piscataway, N.J., pp. 232-236.

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Title Application of growing self-organizing map to distinguish between finger tapping and non tapping from brain images
Author(s) Huang, Pin
Pathirana, Pubudu
Alahakoon, Damminda
Brotchie, Peter
Conference name IEEE Information and Automation for Sustainability. Conference (6th : 2012 : Beijing, China)
Conference location Beijing, China
Conference dates 27-29 Sep. 2012
Title of proceedings ICIAfS 2012 : Proceedings of the 2012 IEEE 6th International Conference on Information and Automation for Sustainability
Editor(s) [Unknown]
Publication date 2012
Conference series IEEE Information and Automation for Sustainability Conference
Start page 232
End page 236
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Growing self-organizing map (GSOM) has been characterized as a knowledge discovery visualization application which outshines the traditional self-organizing map (SOM) due to its dynamic structure in which nodes can grow based on the input data. GSOM is utilized as a visualization tool in this paper to cluster fMRI finger tapping and non- tapping data, demonstrating the visualization capability to distinguish between tapping or non-tapping. A unique feature of GSOM is a parameter called the spread factor whose functionality is to control the spread of the GSOM map. By setting different levels of spread factor, different granularities of region of interests within tapping or non-tapping images can be visualized and analyzed. Euclidean distance based similarity calculation is used to quantify the visualized difference between tapping and non tapping images. Once the differences are identified, the spread factor is used to generate a more detailed view of those regions to provide a better visualization of the brain regions.
ISBN 1467319759
9781467319751
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 899999 Information and Communication Services not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051053

Document type: Conference Paper
Collection: School of Engineering
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