Development of a scaled conjugate gradient algorithm for significant RF neural signal processing

Artificial Neural Networks (ANN) are computational models inspired by the human brain, capable of recognizing patterns and making predictions. Scale Conjugate Gradient (SCG) algorithm is an efficient training method for ANN that accelerates the learning process and improves output accuracy. However, conventional ANN training methods often struggle with slow convergence and can be less accurate when analyzing complex, high-dimensional data such as Electroencephalogram (EEG) signals. Furthermore, the precise classification of subtle neural pattern changes induced by Radiofrequency (RF) exposure remains a significant challenge. SCG improves the learning process of ANNs by speeding up the adjustment of their internal weights, helping the network learn faster and more accurately from large data sets. This study aims to improve the classification of RF neural data patterns using SCG. EEG neural data was captured in sessions before, during and after RF exposure. Power Asymmetry Ratio (PAR) was used for feature extraction. The data involved 96 subjects, were split into 70:30 ratio for training and testing in ANN modelling. The SCG algorithm was integrated, initialized with one hidden layer of 10 neurons. Parameter adjustments were made to optimize convergence, potentially involving multiple layers for model refinement. The results show that RF exposure in During session produces significantly distinct neural patterns, enabling the highest ANN classification accuracy.