Periodogram Connectivity of EEG Signals for the Detection of Dyslexia

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 ex...

Full description

Autores:
Martinez-Murcia, F. J.
Ortiz, A.
Morales-Ortega, R.
López, P. J.
Duque, J. L.
Castillo-Barnes, D.
Segovia, F.
Illan, I. A.
Ortega, J.
Ramírez, J.
Gorriz, J. M.
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7473
Acceso en línea:
https://hdl.handle.net/11323/7473
https://doi.org/10.1007/978-3-030-19591-5_36
https://repositorio.cuc.edu.co/
Palabra clave:
Periodogram
EEG
Connectivity
Principal Component Analysis
Dyslexia
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_94d2d7de0c4e7805d0db5a24085f94e7
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7473
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Periodogram Connectivity of EEG Signals for the Detection of Dyslexia
title Periodogram Connectivity of EEG Signals for the Detection of Dyslexia
spellingShingle Periodogram Connectivity of EEG Signals for the Detection of Dyslexia
Periodogram
EEG
Connectivity
Principal Component Analysis
Dyslexia
title_short Periodogram Connectivity of EEG Signals for the Detection of Dyslexia
title_full Periodogram Connectivity of EEG Signals for the Detection of Dyslexia
title_fullStr Periodogram Connectivity of EEG Signals for the Detection of Dyslexia
title_full_unstemmed Periodogram Connectivity of EEG Signals for the Detection of Dyslexia
title_sort Periodogram Connectivity of EEG Signals for the Detection of Dyslexia
dc.creator.fl_str_mv Martinez-Murcia, F. J.
Ortiz, A.
Morales-Ortega, R.
López, P. J.
Duque, J. L.
Castillo-Barnes, D.
Segovia, F.
Illan, I. A.
Ortega, J.
Ramírez, J.
Gorriz, J. M.
dc.contributor.author.spa.fl_str_mv Martinez-Murcia, F. J.
Ortiz, A.
Morales-Ortega, R.
López, P. J.
Duque, J. L.
Castillo-Barnes, D.
Segovia, F.
Illan, I. A.
Ortega, J.
Ramírez, J.
Gorriz, J. M.
dc.subject.spa.fl_str_mv Periodogram
EEG
Connectivity
Principal Component Analysis
Dyslexia
topic Periodogram
EEG
Connectivity
Principal Component Analysis
Dyslexia
description 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.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-11-24T16:32:31Z
dc.date.available.none.fl_str_mv 2020-11-24T16:32:31Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7473
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-3-030-19591-5_36
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/7473
https://doi.org/10.1007/978-3-030-19591-5_36
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. (2017). https://ezproxy.cuc.edu.co:2067/10.1016/j.compbiomed.2017.09.017
2. 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)
3. 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://ezproxy.cuc.edu.co:2067/10.1121/1.5026239
4. De la Hoz, E., de la Hoz, E., Ortiz, A., Ortega, J., Martínez-Álvarez, A.: Feature selection by multi-objective optimisation: application to network anomaly detection by hierarchical self-organising maps. Knowl.-Based Syst. 71, 322–338 (2014)
5. Illán, I., et al.: 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Inf. Sci. 181(4), 903–916 (2011)
6. Lafuente, V., Gorriz, J.M., Ramirez, J., Gonzalez, E.: P300 brainwave extraction from EEG signals: an unsupervised approach. Expert Syst. Appl. 74, 1–10 (2017). https://ezproxy.cuc.edu.co:2067/10.1016/j.eswa.2016.12.038
7. Ledoit, O., Wolf, M.: A well-conditioned estimator for large-dimensional covariance matrices. J. Multivar. Anal. 88(2), 365–411 (2004). https://ezproxy.cuc.edu.co:2067/10.1016/s0047-259x(03)00096-4
8. Markiewicz, P., Matthews, J., Declerck, J., Herholz, K.: Robustness of multivariate image analysis assessed by resampling techniques and applied to FDG-PET scans of patients with Alzheimer’s disease. Neuroimage 46, 472–485 (2009). http://ezproxy.cuc.edu.co:2053/science/article/B6WNP-4VFK7X3-3/2/e7833cb1d62f98e28326352e45981d00
9. Martínez-Murcia, F., Górriz, J., Ramírez, J., Puntonet, C., Salas-González, D.: Computer aided diagnosis tool for Alzheimer’s disease based on Mann-Whitney-Wilcoxon U-test. Expert Syst. Appl. 39(10), 9676–9685 (2012). https://ezproxy.cuc.edu.co:2067/10.1016/j.eswa.2012.02.153
10. Ortiz, A., Munilla, J., Martínez-Murcia, F.J., Górriz, J.M., Ramírez, J.: Learning longitudinal MRI patterns by SICE and deep learning: assessing the Alzheimer’s disease progression. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 413–424. Springer, Cham (2017). https://ezproxy.cuc.edu.co:2067/10.1007/978-3-319-60964-5_36
11. Peterson, R., Pennington, B.: Developmental dyslexia. Lancet 379, 1997–2007 (2012)
12. Sakkalis, V.: Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput. Biol. Med. 41(12), 1110–1117 (2011). https://ezproxy.cuc.edu.co:2067/10.1016/j.compbiomed.2011.06.020
13. Schoffelen, J.M., Gross, J.: Source connectivity analysis with MEG and EEG. Hum. Brain Mapp. 30(6), 1857–1865 (2009). https://ezproxy.cuc.edu.co:2067/10.1002/hbm.20745
14. Stoeckel, J., Ayache, N., Malandain, G., Koulibaly, P.M., Ebmeier, K.P., Darcourt, J.: Automatic classification of SPECT images of Alzheimer’s disease patients and control subjects. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 654–662. Springer, Heidelberg (2004). https://ezproxy.cuc.edu.co:2067/10.1007/978-3-540-30136-3_80
15. Thompson, P.A., Hulme, C., Nash, H.M., Gooch, D., Hayiou-Thomas, E., Snowling, M.J.: Developmental dyslexia: predicting individual risk. J. Child Psychol. Psychiatry 56(9), 976–987 (2015)
16. Welch, P.: The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967). https://ezproxy.cuc.edu.co:2067/10.1109/tau.1967.1161901
17. Zhou, S.M., Gan, J.Q., Sepulveda, F.: Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface. Inf. Sci. 178(6), 1629–1640 (2008). https://ezproxy.cuc.edu.co:2067/10.1016/j.ins.2007.11.012
dc.rights.spa.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.source.spa.fl_str_mv Lecture Notes in Computer Science
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv https://link.springer.com/chapter/10.1007/978-3-030-19591-5_36
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/bb4f88f0-cddd-480f-b337-dacfafa7e70c/download
https://repositorio.cuc.edu.co/bitstreams/6ec5a6b3-8234-4a24-95b8-92c469664f81/download
https://repositorio.cuc.edu.co/bitstreams/63bd1a35-2c08-424a-84f0-2cdfe5f15601/download
https://repositorio.cuc.edu.co/bitstreams/63419d01-860c-464b-bd9d-977ebc7d3609/download
https://repositorio.cuc.edu.co/bitstreams/393aaa48-0294-44d2-966d-bdcc5eca9889/download
bitstream.checksum.fl_str_mv e19b84c8e5383031053d363f83f7c361
4460e5956bc1d1639be9ae6146a50347
e30e9215131d99561d40d6b0abbe9bad
28c387227ae5358373be7ce79915a59a
f5771fea930e9873a9cdc84a0fb9124f
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1811760843387305984
spelling Martinez-Murcia, F. J.Ortiz, A.Morales-Ortega, R.López, P. J.Duque, J. L.Castillo-Barnes, D.Segovia, F.Illan, I. A.Ortega, J.Ramírez, J.Gorriz, J. M.2020-11-24T16:32:31Z2020-11-24T16:32:31Z2019https://hdl.handle.net/11323/7473https://doi.org/10.1007/978-3-030-19591-5_36Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Martinez-Murcia, F. J.Ortiz, A.Morales-Ortega, R.López, P. J.Duque, J. L.Castillo-Barnes, D.Segovia, F.Illan, I. A.Ortega, J.Ramírez, J.Gorriz, J. M.application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Computer Sciencehttps://link.springer.com/chapter/10.1007/978-3-030-19591-5_36PeriodogramEEGConnectivityPrincipal Component AnalysisDyslexiaPeriodogram Connectivity of EEG Signals for the Detection of DyslexiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. (2017). https://ezproxy.cuc.edu.co:2067/10.1016/j.compbiomed.2017.09.0172. 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)3. 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://ezproxy.cuc.edu.co:2067/10.1121/1.50262394. De la Hoz, E., de la Hoz, E., Ortiz, A., Ortega, J., Martínez-Álvarez, A.: Feature selection by multi-objective optimisation: application to network anomaly detection by hierarchical self-organising maps. Knowl.-Based Syst. 71, 322–338 (2014)5. Illán, I., et al.: 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Inf. Sci. 181(4), 903–916 (2011)6. Lafuente, V., Gorriz, J.M., Ramirez, J., Gonzalez, E.: P300 brainwave extraction from EEG signals: an unsupervised approach. Expert Syst. Appl. 74, 1–10 (2017). https://ezproxy.cuc.edu.co:2067/10.1016/j.eswa.2016.12.0387. Ledoit, O., Wolf, M.: A well-conditioned estimator for large-dimensional covariance matrices. J. Multivar. Anal. 88(2), 365–411 (2004). https://ezproxy.cuc.edu.co:2067/10.1016/s0047-259x(03)00096-48. Markiewicz, P., Matthews, J., Declerck, J., Herholz, K.: Robustness of multivariate image analysis assessed by resampling techniques and applied to FDG-PET scans of patients with Alzheimer’s disease. Neuroimage 46, 472–485 (2009). http://ezproxy.cuc.edu.co:2053/science/article/B6WNP-4VFK7X3-3/2/e7833cb1d62f98e28326352e45981d009. Martínez-Murcia, F., Górriz, J., Ramírez, J., Puntonet, C., Salas-González, D.: Computer aided diagnosis tool for Alzheimer’s disease based on Mann-Whitney-Wilcoxon U-test. Expert Syst. Appl. 39(10), 9676–9685 (2012). https://ezproxy.cuc.edu.co:2067/10.1016/j.eswa.2012.02.15310. Ortiz, A., Munilla, J., Martínez-Murcia, F.J., Górriz, J.M., Ramírez, J.: Learning longitudinal MRI patterns by SICE and deep learning: assessing the Alzheimer’s disease progression. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 413–424. Springer, Cham (2017). https://ezproxy.cuc.edu.co:2067/10.1007/978-3-319-60964-5_3611. Peterson, R., Pennington, B.: Developmental dyslexia. Lancet 379, 1997–2007 (2012)12. Sakkalis, V.: Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput. Biol. Med. 41(12), 1110–1117 (2011). https://ezproxy.cuc.edu.co:2067/10.1016/j.compbiomed.2011.06.02013. Schoffelen, J.M., Gross, J.: Source connectivity analysis with MEG and EEG. Hum. Brain Mapp. 30(6), 1857–1865 (2009). https://ezproxy.cuc.edu.co:2067/10.1002/hbm.2074514. Stoeckel, J., Ayache, N., Malandain, G., Koulibaly, P.M., Ebmeier, K.P., Darcourt, J.: Automatic classification of SPECT images of Alzheimer’s disease patients and control subjects. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 654–662. Springer, Heidelberg (2004). https://ezproxy.cuc.edu.co:2067/10.1007/978-3-540-30136-3_8015. Thompson, P.A., Hulme, C., Nash, H.M., Gooch, D., Hayiou-Thomas, E., Snowling, M.J.: Developmental dyslexia: predicting individual risk. J. Child Psychol. Psychiatry 56(9), 976–987 (2015)16. Welch, P.: The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967). https://ezproxy.cuc.edu.co:2067/10.1109/tau.1967.116190117. Zhou, S.M., Gan, J.Q., Sepulveda, F.: Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface. Inf. Sci. 178(6), 1629–1640 (2008). https://ezproxy.cuc.edu.co:2067/10.1016/j.ins.2007.11.012PublicationORIGINALPeriodogram Connectivity of EEG Signals for the Detection of Dyslexia.pdfPeriodogram Connectivity of EEG Signals for the Detection of Dyslexia.pdfapplication/pdf88919https://repositorio.cuc.edu.co/bitstreams/bb4f88f0-cddd-480f-b337-dacfafa7e70c/downloade19b84c8e5383031053d363f83f7c361MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/6ec5a6b3-8234-4a24-95b8-92c469664f81/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/63bd1a35-2c08-424a-84f0-2cdfe5f15601/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILPeriodogram Connectivity of EEG Signals for the Detection of Dyslexia.pdf.jpgPeriodogram Connectivity of EEG Signals for the Detection of Dyslexia.pdf.jpgimage/jpeg36745https://repositorio.cuc.edu.co/bitstreams/63419d01-860c-464b-bd9d-977ebc7d3609/download28c387227ae5358373be7ce79915a59aMD54TEXTPeriodogram Connectivity of EEG Signals for the Detection of Dyslexia.pdf.txtPeriodogram Connectivity of EEG Signals for the Detection of Dyslexia.pdf.txttext/plain1411https://repositorio.cuc.edu.co/bitstreams/393aaa48-0294-44d2-966d-bdcc5eca9889/downloadf5771fea930e9873a9cdc84a0fb9124fMD5511323/7473oai:repositorio.cuc.edu.co:11323/74732024-09-17 14:09:29.802http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares