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

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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.

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Authors and Affiliations

  1. Robotics Research Division, Daegu Gyeongbuk Institute of Science & Technology, Daegu, 711-873, Korea

    Jinungn An

  2. Department of Computer Engineering, Sungkyunkwan University, Suwon, 440-746, Korea

    JongHyun Lee & ChangWook Ahn

Authors
  1. Jinungn An
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  2. JongHyun Lee
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  3. ChangWook Ahn
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Correspondence to ChangWook Ahn.

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An, J., Lee, J. & Ahn, C. An efficient GP approach to recognizing cognitive tasks from fNIRS neural signals. Sci. China Inf. Sci. 56, 1–7 (2013). https://doi.org/10.1007/s11432-013-5001-8

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  • DOI: https://doi.org/10.1007/s11432-013-5001-8

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