Cited 8 time in webofscience Cited 7 time in scopus

An efficient GP approach to recognizing cognitive tasks from fNIRS neural signals

Title
An efficient GP approach to recognizing cognitive tasks from fNIRS neural signals
Authors
An, J[An, Jinungn]Lee, J[Lee, JongHyun]Ahn, C[Ahn, ChangWook]
DGIST Authors
An, J[An, Jinungn]
Issue Date
2013-10
Citation
Science China: Information Sciences, 56(10)
Type
Article
Article Type
Article
Keywords
ClassificationClassification (of Information)Cognitive TaskDestructive ProcessfNIRSForestryFunctional NeuroimagingGenetic ProgrammingInformation RetrievalMathematicsMulti-Class ProblemsMulti-Tree RepresentationNeural SignalsNon-StationarySub TreesTreesTrees (Mathematics)
ISSN
1674-733X
Abstract
This paper presents a new genetic programming (GP) approach to accurately classifying cognitive tasks from non-stationary and noisy fNIRS neural signals. To this end, a new GP that effectively handles multiclass problems is developed. In accordance with multi-tree structure, GP operators are innovated: crossover exchanges every subtree of parents without suffering from any incongruity problem and mutation fine-tunes candidate solutions by a less destructive process. Experimental results verifies the effectiveness of the proposed GP classifier over existing references. © 2013 Science China Press and Springer-Verlag Berlin Heidelberg.
URI
http://hdl.handle.net/20.500.11750/1602
DOI
10.1007/s11432-013-5001-8
Publisher
SCIENCE PRESS
Related Researcher
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Wellness1. Journal Articles


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