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An efficient GP approach to recognizing cognitive tasks from fNIRS neural signals
- An efficient GP approach to recognizing cognitive tasks from fNIRS neural signals
- An, J[An, Jinungn]; Lee, J[Lee, JongHyun]; Ahn, C[Ahn, ChangWook]
- DGIST Authors
- An, J[An, Jinungn]
- Issue Date
- Science China: Information Sciences, 56(10)
- Article Type
- Classification; Classification (of Information); Cognitive Task; Destructive Process; fNIRS; Forestry; Functional Neuroimaging; Genetic Programming; Information Retrieval; Mathematics; Multi-Class Problems; Multi-Tree Representation; Neural Signals; Non-Stationary; Sub Trees; Trees; Trees (Mathematics)
- 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.
- SCIENCE PRESS
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