Synthetic_Multimodal_Dataset_Dyslexia.csv

This dataset is <b>synthetic</b>, generated for the purpose of our on-going research titled <i>"Dyslexia Detection Using Multi-Modality Data."</i> The dataset was created to simulate realistic scenarios by incorporating key characteristics of individuals with dyslexia (abnormal group) and those without dyslexia (normal group).The generation process was guided by:<b>Known Characteristics of Dyslexic Individuals:</b>Handwriting patterns, including irregular pressure, inconsistent stroke velocity, and wider variability in letter spacing.Distinct EEG activity patterns, such as heightened theta and altered alpha frequencies.Behavioral indicators like slower eye blink rates and reduced smile scores during cognitive tasks.<b>Control Characteristics for Normal Individuals:</b>More consistent handwriting patterns and EEG signal distributions.Behavioral indicators aligned with typical age-appropriate cognitive and emotional responses.This synthetic dataset, therefore, allows us to explore multimodal patterns relevant to dyslexia detection while ensuring complete privacy and ethical compliance.Research UtilizationThe dataset forms the foundation of our study, enabling the development of machine learning models that can:Differentiate between dyslexic and non-dyslexic individuals.Leverage multimodal features for improved accuracy and robustness in dyslexia detection.