Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis
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AuthorFormoso, Marco A.; Ortiz, Andrés; Martínez Murcia, Francisco Jesús; Gallego, Nicolás; Luque, Juan L.
Functional connectivityEEGAnomaly detectionSelf-organizing mapPhase locking valueCircular correlation
Formoso, M.A.; Ortiz, A.; Martinez-Murcia, F.J.; Gallego, N.; Luque, J.L. Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis. Sensors 2021, 21, 7061. https://doi. org/10.3390/s21217061
SponsorshipPGC2018-098813-B-C32 (Spanish “Ministerio de Ciencia, Innovación y Universidades”); UMA20-FEDERJA-086 (Consejería de econnomía y conocimiento, Junta de Andalucía); European Regional Development Funds (ERDF); Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”.
Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult to extract information about the brain processes involved in the different tasks and, then, to go deeper into its biological basis. Thus, the use of biomarkers can contribute not only to the diagnosis but also to a better understanding of specific learning disorders such as dyslexia. In this work, we use Electroencephalography (EEG) signals to discover differences among controls and dyslexic subjects using signal processing and artificial intelligence techniques. Specifically, we measure phase synchronization among channels, to reveal the functional brain network activated during auditory processing. On the other hand, to explore synchronicity patterns risen by low-level auditory processing, we used specific stimuli consisting in band-limited white noise, modulated in amplitude at different frequencies. The differential information contained in the functional (i.e., synchronization) network has been processed by an anomaly detection system that addresses the problem of subjects variability by an outlier-detection method based on vector quantization. The results, obtained for 7 years-old children, show that the proposed method constitutes an useful tool for clinical use, with the area under ROC curve (AUC) values up to 0.95 in differential diagnosis tasks.