Nonlinear Brain Signal Processing to Model Learning and Memory Processes in Brain

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, ۶۵ men and women with age range of ۱۹-۲۸ years and right hand have been used that ۱۵ 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 ۵ 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 ۷۸.۳۳% 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 ۷۳.۲۷%. 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.

کلیدواژه ها:

Statistical Modeling ، Learning and Memory Processes ، Ray Auditory Verbal Learning Test (RAVLT) ، Higuchi Fractal Dimension