A novel and efficient Wavelet Scattering Transform approach for primitive-stage dyslexia-detection using electroencephalogram signals

Abstract

Dyslexia is a neurological disorder affecting reading and writing abilities. Early intervention is important for affected individuals’ social and academic development. The accuracy and objectivity limitations of traditional dyslexia detection systems based on behavioral symptoms and standard tests can pose challenges to the early detection of the condition. In response, an electroencephalogram (EEG) based detection method has been proposed to aid medical professionals in addressing these limitations. A comparison is made between the Wavelet Scattering Transform (WST) approach and three other approaches, namely Spectral Statistical Features (SSF), Connectivity Features with Autoencoders (CFA), and Hybrid Features (HF), using two datasets. These two datasets were chosen for various reasons, including the fact that they were collected during different tasks and from different countries. Another significant factor is that the age range of the participants was 7 and 12 years old, marking the beginning of their educational journey. This age range is ideal for detecting dyslexia in its primitive stages, making these datasets a perfect fit for this research. The performance evaluation of the approaches involved utilizing Support Vector Machine (SVM) classifiers with three non-linear kernels and k-fold cross-validation implementation. The findings suggest that the other three approaches could not achieve more than 80% accuracy, and their accuracy results were inconsistent with each dataset. In contrast, the WST approach achieved a high accuracy rate, with an average accuracy of 96.96% and 97.12% for dataset 1 and dataset 2, respectively, using the Radial Basis Function (RBF) kernel. The accuracy of WST features is further improved to 98.72% and 98.67% through the Majority Voting method. These findings demonstrate the effectiveness and generalization of the WST approach.