dc.creator | Olías Sánchez, Francisco Javier | es |
dc.creator | Martín Clemente, Rubén | es |
dc.creator | Sarmiento Vega, María Auxiliadora | es |
dc.creator | Cruces Álvarez, Sergio Antonio | es |
dc.date.accessioned | 2022-04-01T15:06:13Z | |
dc.date.available | 2022-04-01T15:06:13Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Olías Sánchez, F.J., Martín Clemente, R., Sarmiento Vega, M.A. y Cruces Álvarez, S.A. (2019). EEG signal processing in mi-bci applications with improved covariance matrix estimators. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 27 (5), 895-904. | |
dc.identifier.issn | 1534-4320 | es |
dc.identifier.uri | https://hdl.handle.net/11441/131688 | |
dc.description | Article number 8688582 | es |
dc.description.abstract | n brain–computer interfaces (BCIs), the
typical models of the EEG observations usually lead to
a poor estimation of the trial covariance matrices, given
the high non-stationarity of the EEG sources. We propose
the application of two techniques that significantly improve
the accuracy of these estimations and can be combined
with a wide range of motor imagery BCI (MI-BCI) methods.
The first one scales the observations in such a way that
implicitly normalizes the common temporal strength of the
source activities. When the scaling applies independently
to the trials of the observations, the procedure justifies
and improves the classical preprocessing for the EEG data.
In addition, when the scaling is instantaneous and inde pendent for each sample, the procedure particularizes to
Tyler’s method in statistics for obtaining a distribution free estimate of scattering. In this case, the proposal pro vides an original interpretation of this existing method
as a technique that pursuits an implicit instantaneous
power-normalization of the underlying source processes.
The second technique applies to the classifier and improves
its performance through a convenient regularization of
the features covariance matrix. Experimental tests reveal
that a combination of the proposed techniques with the
state-of-the-art algorithms for motor-imagery classification
provides a significant improvement in the classification
results. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad ( España) TEC2017-82807-P | es |
dc.format | application/pdf | es |
dc.format.extent | 10 p. | es |
dc.language.iso | eng | es |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | es |
dc.relation.ispartof | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 27 (5), 895-904. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Common spatial pattern | es |
dc.subject | Brain–computer interfaces | es |
dc.subject | Motor-imagery classification | es |
dc.subject | Covariance matrix estimation | es |
dc.title | EEG signal processing in mi-bci applications with improved covariance matrix estimators | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones | es |
dc.relation.projectID | TEC2017-82807-P | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/abstract/document/8688582 | es |
dc.identifier.doi | 10.1109/TNSRE.2019.2905894 | es |
dc.journaltitle | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING | es |
dc.publication.volumen | 27 | es |
dc.publication.issue | 5 | es |
dc.publication.initialPage | 895 | es |
dc.publication.endPage | 904 | es |
dc.contributor.funder | Ministerio de Economía y Competitividad ( España) | es |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | es |