Early detection of ADHD and Dyslexia from EEG Signals

Abstract

A learning impairment is a dysfunction in one or more fundamental psychological functions that might show up as a lack of proficiency in some areas of learning, such reading, writing, while doing mathematical calculations or while coordinating movements. Learning disabilities are typically not identified until the kid is of school age, Although they can also be developed in very young infants.We aim to develop a machine learning model to analyze EEG (electroencephalogram) signals from people with learning difficulties and provide results in minutes with the highest level of accuracy. Here we will be considering Learning disabilities namely Dyslexia and ADHD(Attention Deficit Hyperactivity Disorder). For the early detection of these disabilities, machine learning algorithms like Support vector machines, K-nearest neighbors, Random Forest, Decision Trees, and convolutional neural networks were used. In order to determine which lobe combination provides the maximum accuracy, we tested the ADHD model using a variety of lobe combinations. The finding indicated that EEG signals produced the highest classification accuracy and Machine learning applications have high potential in identifying ADHD and Dyslexia.