Motor-imagery-based brain-computer interface using signal derivation and aggregation functions
| dc.contributor.author | Fumanal Idocin, Javier | |
| dc.contributor.author | Wang, Yu-Kai | |
| dc.contributor.author | Lin, Chin-Teng | |
| dc.contributor.author | Fernández Fernández, Francisco Javier | |
| dc.contributor.author | Sanz Delgado, José Antonio | |
| dc.contributor.author | Bustince Sola, Humberto | |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.date.accessioned | 2022-05-10T11:00:35Z | |
| dc.date.available | 2022-05-25T23:00:14Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Brain Computer Interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is Motor Imagery (MI). In BCI applications, the ElectroEncephaloGraphy (EEG) is a very popular measurement for brain dynamics because of its non-invasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-tonoise ratio of these signals and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal pre-processing, feature extraction and decision making. In this paper, we define a new BCI Framework, named Enhanced Fusion Framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. Firstly, we include an additional pre-processing step of the signal: a differentiation of the EEG signal that makes it time-invariant. Secondly, we add an additional frequency band as feature for the system: the Sensory Motor Rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing motor imagery-based braincomputer interface experiments. On this dataset, the new system achieved a 88.80% of accuracy. We also propose an optimized version of our system that is able to obtain up to 90, 76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results. | en |
| dc.description.sponsorship | The work of Javier Fumanal Idocin, Javier Fernández, José Antonio Sanz, and Humberto Bustince was supported by the Project PID2019-108392GB I00 (AEI/10.13039/501100011033). | en |
| dc.embargo.lift | 2022-05-25 | |
| dc.embargo.terms | 2022-05-25 | |
| dc.format.extent | 13 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | J. Fumanal-Idocin, Y. -K. Wang, C. -T. Lin, J. Fernández, J. A. Sanz and H. Bustince, 'Motor-Imagery-Based Brain-Computer Interface Using Signal Derivation and Aggregation Functions,' in IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2021.3073210. | en |
| dc.identifier.doi | 10.1109/TCYB.2021.3073210 | |
| dc.identifier.issn | 2168-2267 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/42912 | |
| dc.language.iso | eng | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartof | IEEE Transactions on Cybernetics (2021) | en |
| dc.relation.publisherversion | https://doi.org/10.1109/TCYB.2021.3073210 | |
| dc.rights | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Brain-computer-interface (BCI) | en |
| dc.subject | Motor imagery (MI) | en |
| dc.subject | Classification | en |
| dc.subject | Aggregation functions | en |
| dc.subject | Information fusion | en |
| dc.subject | Signal processing | en |
| dc.title | Motor-imagery-based brain-computer interface using signal derivation and aggregation functions | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 5193d488-fd4e-4556-88ca-ba5116412a36 | |
| relation.isAuthorOfPublication | 741321a5-40af-41aa-bacb-5da283dd18ab | |
| relation.isAuthorOfPublication | 04db2b7d-89dc-4815-be4a-4b201cdce99b | |
| relation.isAuthorOfPublication | 1bdd7a0e-704f-48e5-8d27-4486444f82c9 | |
| relation.isAuthorOfPublication.latestForDiscovery | 5193d488-fd4e-4556-88ca-ba5116412a36 |