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This study used EEG recordings to detect dyslexia at a young age. EEG recordings of 53 individuals, including 29 dyslexic and 24 normal individuals, were collected while they were engaged in two distinct mental activities known as the N-Back task and the Oddball task. Predictors were extracted using several methods and reduced using Principal Component Analysis (PCA). A relief-based strategy was applied to select predictors, and Support Vector Machine (SVM) classifier was used to achieve an average accuracy of 79.3% for dyslexia detection, which is better than the performance of its predecessors. The results indicate that EEG recordings and machine learning methods could be useful for identifying dyslexia in children.<\/jats:p>","DOI":"10.1007\/s44163-023-00082-4","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T11:02:49Z","timestamp":1697713369000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Early detection of dyslexia based on EEG with novel predictor extraction and selection"],"prefix":"10.1007","volume":"3","author":[{"given":"Shankar","family":"Parmar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chirag","family":"Paunwala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,19]]},"reference":[{"key":"82_CR1","unstructured":"Dyslexia YCF. 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