Extraction of discriminative features from EEG signals of dyslexic children; before and after the treatment

• Published in: Cognitive neurodynamics 2022-12, Vol.16 (6), p.1249-1259
• Main Authors: Oliaee, Anahita, Mohebbi, Maryam, Shirani, Sepehr, Rostami, Reza
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brain research; Children; Classification; Cognitive ability; Cognitive Psychology; Computer Science; Direct current; Dyslexia; EEG; Electrical stimulation of the brain; Electrodes; Electroencephalography; ESB; Learning; Magnetic resonance imaging; Methods; Neurological diseases; Neurosciences; Occupational therapy; Principal components analysis; Reading; Research Article; Skills; Support vector machines
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-022-09794-2

Dyslexia is a neurological disorder manifested as difficulty reading and writing. It can occur despite adequate instruction, intelligence, and intact sensory abilities. Different electroencephalogram (EEG) patterns have been demonstrated between dyslexic and healthy subjects in previous studies. This study focuses on the difference between patients before and after treatment. The main goal is to identify the subset of features that adequately discriminate subjects before and after a specific treatment plan. The treatment consists of Transcranial Direct Current Stimulation (tDCS) and occupational therapy using the BrainWare SAFARI software. The EEG signals of sixteen dyslexic children were recorded during the eyes-closed resting state before and after treatment. The preprocessing step was followed by the extraction of a wide range of features to investigate the differences related to the treatment. An optimal subset of features extracted from recorded EEG signals was determined using Principal Component Analysis (PCA) in conjunction with the Sequential Floating Forward Selection (SFFS) algorithm. The results showed that treatment leads to significant changes in EEG features like spectral and phase-related EEG features, in various regions. It has been demonstrated that the extracted subset of discriminative features can be useful for classification applications in treatment assessment. The most discriminative subset of features could classify the data with an accuracy of 92% with SVM classifier. The above result confirms the efficacy of the treatment plans in improving dyslexic children's cognitive skills.