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

conference contribution
posted on 2012-01-01, 00:00 authored by P Huang, Pubudu PathiranaPubudu Pathirana, D Alahakoon, P Brotchie
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.

History

Event

IEEE Information and Automation for Sustainability. Conference (6th : 2012 : Beijing, China)

Pagination

232 - 236

Publisher

IEEE

Location

Beijing, China

Place of publication

Piscataway, N.J.

Start date

2012-09-27

End date

2012-09-29

ISBN-13

9781467319751

ISBN-10

1467319759

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

ICIAfS 2012 : Proceedings of the 2012 IEEE 6th International Conference on Information and Automation for Sustainability

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