Periodogram Connectivity of EEG Signals for the Detection of Dyslexia
| Autor: | Ignacio A. Illán, Javier Ramírez, Julio Ortega, Juan Manuel Górriz, Juan Luis Luque, Fermín Segovia, Diego Castillo-Barnes, Roberto Cesar Morales-Ortega, Francisco Jesús Martínez-Murcia, P. J. López, Andrés Ortiz |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Brain activity and meditation Population 02 engineering and technology Electroencephalography 03 medical and health sciences 0302 clinical medicine Discriminative model 0202 electrical engineering electronic engineering information engineering medicine education Mental age education.field_of_study medicine.diagnostic_test business.industry Dyslexia Pattern recognition medicine.disease Support vector machine Learning disability Developmental dyslexia 020201 artificial intelligence & image processing Artificial intelligence medicine.symptom business 030217 neurology & neurosurgery |
| Zdroj: | Understanding the Brain Function and Emotions ISBN: 9783030195908 IWINAC (1) |
| DOI: | 10.1007/978-3-030-19591-5_36 |
| Popis: | Electroencephalography (EEG) signals provide an important source of information of brain activity at different areas. This information can be used to diagnose brain disorders according to different activation patterns found in controls and patients. This acquisition technology can be also used to explore the neural basis of less evident learning disabilities such as Developmental Dyslexia (DD). DD is a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling, whose prevalent is estimated between 5% and 12% of the population. In this paper we propose a method to extract discriminative features from EEG signals based on the relationship among the spectral density at each channel. This relationship is computed by means of different correlation measures, inferring connectivity-like markers that are eventually selected and classified by a linear support vector machine. The experiments performed shown AUC values up to 0.7, demonstrating the applicability of the proposed approach for objective DD diagnosis. |
| Databáze: | OpenAIRE |
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