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Nonlinear brain signal processing to model learning and memory processes in brain

    Authors

    • Mohsen Najafi 1
    • Mohammad Reza Arab 2
    • Faezeh Ebrahimi 3

    1 Electrical Eng. Dept, Arak University of Technology (ARAKUT), Arak, Iran, & The Institute for Cognitive and Brain Sciences (ICBS), Shahid Beheshti University, Tehran, Iran

    2 Intelligent Signal Processing and Cognitive Sciences Research Laboratory, Arak University of Technology, Arak, Iran

    3 Faculty of Engineering, Arak University of Technology, Arak, Iran

,
Articles in Press

Document Type : Original Article

10.48308/jncog.2025.237954.1008
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Abstract

Learning can be seen as a process of obtaining stable brain information that shows itself in thought, experiences, or behavior, and there is local memory for storing and retrieving information. In a nutshell, learning is a process that defines its output, memory. In this study, the behavior of learning and memory of ray was investigated along with dynamic analysis of signals. In this study, to create learning and memory processes, the Rey Auditory Verbal Learning Test (RAVLT) is used. In this study, 65 men and women with age range of 19-28 years and right hand have been used that 15 words in each stage are repeated for the subject and in each step one needs to repeat it and repeat the EEG signals during these eight stages and before and after these stages in non-task relaxation conditions and 5 sub-frequency bands are analyzed. The statistical multivariate model demonstrates a correlation between behavioral learning rate (RAVLT) and three key parameters: the Hurst exponent, Higuchi fractal dimension, and approximate entropy of brain signals. The regression model highlights that the most significant predictor of learning and memory formation is the Higuchi fractal dimension of the signals, achieving an accuracy of 78.33% in predicting the learning rate. A second multivariate model, designed to estimate the behavioral learning rate based on the fractal dimensions of the sub-bands, achieves an accuracy of 73.27%. In this second model, the delta and theta frequency sub-bands exhibit the greatest effect size and the largest coefficients for predicting the learning rate. The study of this research shows that learning enhancement and synchronization process simultaneously reduces the entropy of approximation and the fractal analysis followed by Higuchi model which is the result of organizing the brain in information processing.

Keywords

  • Statistical Modeling
  • Learning and Memory Processes
  • Ray Auditory Verbal Learning Test (RAVLT)
  • Higuchi Fractal Dimension
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Journal of Neurodevelopmental Cognition

Articles in Press, Accepted Manuscript
Available Online from 30 March 2025
Files
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  • PDF 2.92 M
History
  • Receive Date: 14 December 2024
  • Revise Date: 04 February 2025
  • Accept Date: 27 February 2025
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How to cite
  • RIS
  • EndNote
  • Mendeley
  • BibTeX
  • APA
  • MLA
  • HARVARD
  • CHICAGO
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Statistics
  • Article View: 123
  • PDF Download: 67

APA

Najafi, M. , Arab, M. R. and Ebrahimi, F. (2025). Nonlinear brain signal processing to model learning and memory processes in brain. Journal of Neurodevelopmental Cognition, (), -. doi: 10.48308/jncog.2025.237954.1008

MLA

Najafi, M. , , Arab, M. R. , and Ebrahimi, F. . "Nonlinear brain signal processing to model learning and memory processes in brain", Journal of Neurodevelopmental Cognition, , , 2025, -. doi: 10.48308/jncog.2025.237954.1008

HARVARD

Najafi, M., Arab, M. R., Ebrahimi, F. (2025). 'Nonlinear brain signal processing to model learning and memory processes in brain', Journal of Neurodevelopmental Cognition, (), pp. -. doi: 10.48308/jncog.2025.237954.1008

CHICAGO

M. Najafi , M. R. Arab and F. Ebrahimi, "Nonlinear brain signal processing to model learning and memory processes in brain," Journal of Neurodevelopmental Cognition, (2025): -, doi: 10.48308/jncog.2025.237954.1008

VANCOUVER

Najafi, M., Arab, M. R., Ebrahimi, F. Nonlinear brain signal processing to model learning and memory processes in brain. Journal of Neurodevelopmental Cognition, 2025; (): -. doi: 10.48308/jncog.2025.237954.1008

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