EEG Signal Analysis of Real-word Reading and Nonsense-word Reading between Adults with Dyslexia and without Dyslexia
The evolution in technology plays a major role in improving diagnostic accuracies. Pattern recognition and classification are techniques that may help uncover answers that are not always obvious. This paper attempts to discover such patterns found in brain wave signals in people with dyslexia using classifiers. Electroencephalogram (EEG) signals captured during real-word and nonsense-word reading activities from individuals with dyslexia are compared with normal controls. The classification was performed using Linear Support Vector Machine (LSVM) and Cubic Support Vector Machine (CSVM) on different lobes of the brain. The study revealed that the CSVM classifier could produce superior validation accuracies compared to the LSVM classifier. Further, the nonsense-words classifiers presented significant validation accuracies compared to real-words classifiers.