Dyslexia Diagnosis using the EEG Signal: A Machine Learning Approach
Authors: Al-Barhamtoshy Hassanin , Motaweh Diaa-Elden M. 
Published: December 2024

Dyslexia is a learning disorder impacting reading, writing, calculation, memory, and spelling abilities. It is a neurodevelopmental condition affecting approximately 5-10% of the population. The use of eye tracking combined with machine learning algorithms for dyslexia detection is an emerging and promising approach. This method has shown high accuracy in diagnosing dyslexia among students without the need for traditional classification techniques. The article explores how this innovative technique is utilized by various medical professionals to identify dyslexia in individuals to enhance their quality of life. Challenges related to reading, writing, speaking, and spelling are common among many individuals; hence, the utilization of eye-tracking analysis and machine learning methods for dyslexia identification is crucial. A novel supervised learning classifier approach will be introduced based on online data consisting of eye-tracking reading videos from 98 dyslexic and 88 non-dyslexic subjects. Frequency spectrum analysis is conducted using a short-time Fourier transform to assess temporal frequency significance in spectral values with dimension reduction achieved through PCA. Dyslexia assessment typically involves evaluating an individual&#039;s reading, and language skills alongside cognitive and psychological aspects through experiments, observations of behavior patterns, and interviews with the individuals and their families. Electroencephalography (EEG) signals play a vital role in brain-computer interface activities; however, predicting dyslexia can be intricate and resource-intensive. To address this challenge, brain signal data from the Massive Open Online Courses (MOOC) dataset is harnessed to gauge comprehension levels during lecture videos by analyzing attention and mediation parameters based on power spectrum transformed EEG data features extraction followed by frequency bands classification using artificial intelligence for predictive purposes. Binary classification along with deep neural networks leveraging Keras and TensorFlow frameworks are employed in this study.&quot; Binary classification using deep learning has both advantages and disadvantages. This study aims to develop a framework for addressing regression issues by creating a deep neural network. The objective is to design a model capable of analyzing EEG brain signals to predict the presence of dyslexia indicators in individuals. This involves constructing a binary classifier to distinguish between dyslexic and non-dyslexic individuals, to enable early detection for timely interventions. Accurate diagnosis is crucial not only for identifying dyslexia but also for ruling out other conditions that may impact reading, writing, memory, and language skills. Through proper assessment and evaluation, dyslexia can be accurately identified, allowing affected individuals to receive necessary academic and life support. Evaluation metrics such as &quot;Receive Operating Characteristic (ROC)&quot; and Area Under Curve (AUC) will be employed to assess precision, recall, and F1-Score. Additionally, feature selection techniques will be utilized to model input variables from EEG signals about target features, aiding in distinguishing relevant from irrelevant information.