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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 BrotchieGrowing 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.
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Event
IEEE Information and Automation for Sustainability. Conference (6th : 2012 : Beijing, China)Pagination
232 - 236Publisher
IEEELocation
Beijing, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2012-09-27End date
2012-09-29ISBN-13
9781467319751ISBN-10
1467319759Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
ICIAfS 2012 : Proceedings of the 2012 IEEE 6th International Conference on Information and Automation for SustainabilityUsage metrics
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