A comprehensive review of machine learning approaches for dyslexia diagnosis

Electroencephalography (EEG) is the commonly employed electro-biological imaging technique for diagnosing brain functioning. The EEG signals are used to determine head injury, ascertain brain cell functioning, and monitor brain development. EEG can add multiple dimensions towards the identification of learning disability being an abnormality of the brain. Early and accurate detection of brain diseases can significantly reduce the mortality rate with a lesser treatment cost. The machine learning techniques can examine, classify, and process EEG signals to accurately understand brain activities and disorders. This paper is a comprehensive review of the application of machine learning techniques in the classification of EEG signals of dyslexia and analysis of an improved framework to extemporize the classifier's performance and accuracy in discriminating between dyslexics and controls. The presence of noises and artefacts often reduces the performance of classifiers and hampers results. This study reviews input pre-processing, feature selection, feature extraction techniques and machine learning algorithms for the early detection of disorder. The SVM was found to be outperforming other machine learning techniques for the classification of EEG signals.

Automated summary

This comprehensive review discusses the application of machine learning techniques in classifying EEG signals for dyslexia. It also analyzes an improved framework to enhance the performance and accuracy of the classifier in distinguishing between dyslexics and controls. The study provides an overview of input pre-processing, feature selection, feature extraction techniques, and machine learning algorithms for early disorder detection.