Abstract
The search for a dyslexia diagnosis based on exclusively objective methods is currently a challenging task. Usually, this disorder is analyzed by means of behavioral tests prone to errors due to their subjective nature; e.g. the subject’s mood while doing the test can affect the results. Understanding the brain processes involved is key to proportionate a correct analysis and avoid these types of problems. It is in this task, biomarkers like electroencephalograms can help to obtain an objective measurement of the brain behavior that can be used to perform several analyses and ultimately making a diagnosis, keeping the human interaction at minimum. In this work, we used recorded electroencephalograms of children with and without dyslexia while a sound stimulus is played. We aim to detect whether there are significant differences in adaptation when the same stimulus is applied at different times. Our results show that following this process, a machine learning pipeline can be built with AUC values up to 0.73.
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Notes
- 1.
The study was carried out with the understanding and written consent of each child’s legal guardian and in the presence thereof, and was approved by the Medical Ethical Committee of the Malaga University (ref. 16-2020-H) and according to the dispositions of the World Medical Association Declaration of Helsinki.
References
Adeli, H., Zhou, Z., Dadmehr, N.: Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods 123(1), 69–87 (2003)
Bell, M.A., Cuevas, K.: Using EEG to study cognitive development: issues and practices. J. Cogn. Dev. 13(3), 281–294 (2012)
De Vos, A., Vanvooren, S., Vanderauwera, J., Ghesquière, P., Wouters, J.: A longitudinal study investigating neural processing of speech envelope modulation rates in children with (a family risk for) dyslexia. Cortex 93, 206–219 (2017)
Di Liberto, G., Peter, V., Kalashnikova, M., Goswami, U., Burnham, D., Lalor, E.: Atypical cortical entrainment to speech in the right hemisphere underpins phonemic deficits in dyslexia. NeuroImage 175, 70–79 (2018)
Dimitriadis, S.I., Simos, P.G., Fletcher, J.M., Papanicolaou, A.C.: Aberrant resting-state functional brain networks in dyslexia: symbolic mutual information analysis of neuromagnetic signals. Int. J. Psychophysiol. 126, 20–29 (2018)
Flanagan, S., Goswami, U.: The role of phase synchronisation between low frequency amplitude modulations in child phonology and morphology speech tasks. J. Acoust. Soc. Am. 143, 1366–1375 (2018). https://doi.org/10.1121/1.5026239
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 21(21), 7061 (2021)
Gálvez, G., Recuero, M., Canuet, L., Del-Pozo, F.: Short-term effects of binaural beats on eeg power, functional connectivity, cognition, gait and anxiety in Parkinson’s disease. Int. J. Neural Syst. 28(05), 1750055 (2018). pMID: 29297265
Górriz, J.M., et al.: Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications. Neurocomputing 410, 237–270 (2020)
Hülsemann, M.J., Naumann, E., Rasch, B.: Quantification of phase-amplitude coupling in neuronal oscillations: comparison of phase-locking value, mean vector length, modulation index, and generalized-linear-modeling-cross-frequency-coupling. Front. Neurosci. 13, 573 (2019)
Li, R., Principe, J.C.: Blinking artifact removal in cognitive EEG data using ICA. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5273–5276 (2006)
Martinez-Murcia, F.J., et al.: EEG connectivity analysis using denoising autoencoders for the detection of dyslexia. Int. J. Neural Syst. 30(07), 2050037 (2020)
Mirzaei, G., Adeli, A., Adeli, H.: Imaging and machine learning techniques for diagnosis of Alzheimer’s disease. Rev. Neurosci. 27(8), 857–870 (2016)
Molinaro, N., Lizarazu, M., Lallier, M., Bourguignon, M., Carreiras, M.: Out-of-synchrony speech entrainment in developmental dyslexia. Hum. Brain Mapp. 37, 2767–2783 (2016)
Ortiz, A., López, P.J., Luque, J.L., Martínez-Murcia, F.J., Aquino-Britez, D.A., Ortega, J.: An anomaly detection approach for dyslexia diagnosis using EEG signals. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds.) IWINAC 2019, Part I. LNCS, vol. 11486, pp. 369–378. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19591-5_38
Perera, H., Shiratuddin, M.F., Wong, K.W., Fullarton, K.: EEG signal analysis of writing and typing between adults with dyslexia and normal controls. Int. J. Interact. Multimed. Artif. Intell. 5(1), 62 (2018)
Peterson, R., Pennington, B.: Developmental dyslexia. Lancet 379, 1997–2007 (2012)
Stam, C.J., Nolte, G., Daffertshofer, A.: Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum. Brain Mapp. 28(11), 1178–1193 (2007)
Tamboer, P., Vorst, H., Ghebreab, S., Scholte, H.: Machine learning and dyslexia: classification of individual structural neuro-imaging scans of students with and without dyslexia. NeuroImage: Clin. 11, 508–514 (2016)
Acknowledgments
This work was supported by projects PGC2018-098813-B-C32, PGC2018-098813-B-C31 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), UMA20-FEDERJA-086 and P18-RT-1624 (Consejería de economía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF) as well as the BioSiP (TIC-251) research group. M. A. Formoso Grant PRE2019-087350 funded by MCIN/AEI/ 10.13039/501100011033 by “ESF Investing in your future”. Work by F.J.M.M. was supported by the MICINN “Juan de la Cierva - Incorporación” Fellowship. We also thank the Leeduca research group and Junta de Andalucía for the data supplied and the support. Funding for open access charge: Spanish “Ministerio de Ciencia, Innovación y Universidades”, and European Regional Development Funds (ERDF).
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Formoso, M.A., Ortiz, A., Martínez-Murcia, F.J., Brítez, D.A., Escobar, J.J., Luque, J.L. (2022). Temporal Phase Synchrony Disruption in Dyslexia: Anomaly Patterns in Auditory Processing. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_2
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