Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26560
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dc.contributor.authorAdair, Jasonen_UK
dc.contributor.authorBrownlee, Alexanderen_UK
dc.contributor.authorDaolio, Fabioen_UK
dc.contributor.authorOchoa, Gabrielaen_UK
dc.contributor.editorNicosia, Gen_UK
dc.contributor.editorPardalos, Pen_UK
dc.contributor.editorGiuffrida, Gen_UK
dc.contributor.editorUmeton, Ren_UK
dc.date.accessioned2018-04-07T05:03:59Z-
dc.date.available2018-04-07T05:03:59Z-
dc.date.issued2018en_UK
dc.identifier.urihttp://hdl.handle.net/1893/26560-
dc.description.abstractA new proof-of-concept method for optimising the performance of Brain Computer Interfaces (BCI) while minimising the quantity of required training data is introduced. This is achieved by using an evolutionary approach to rearrange the distribution of training instances, prior to the construction of an Ensemble Learning Generic Information (ELGI) model. The training data from a population was optimised to emphasise generality of the models derived from it, prior to a re-combination with participant-specific data via the ELGI approach, and training of classifiers. Evidence is given to support the adoption of this approach in the more difficult BCI conditions: smaller training sets, and those suffering from temporal drift. This paper serves as a case study to lay the groundwork for further exploration of this approach.en_UK
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.relationAdair J, Brownlee A, Daolio F & Ochoa G (2018) Evolving training sets for improved transfer learning in brain computer interfaces. In: Nicosia G, Pardalos P, Giuffrida G & Umeton R (eds.) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science, 10710. MOD 2017 - The Third International Conference on Machine Learning, Optimization and Big Data, Volterra, Italy, 14.09.2017-17.09.2017. Cham, Switzerland: Springer, pp. 186-197. https://link.springer.com/chapter/10.1007/978-3-319-72926-8_16; https://doi.org/10.1007/978-3-319-72926-8_16en_UK
dc.relation.ispartofseriesLecture Notes in Computer Science, 10710en_UK
dc.relation.urihttp://www.taosciences.it/mod/en_UK
dc.rightsPublisher policy allows this work to be made available in this repository. Published in: Nicosia G, Pardalos P, Giuffrida G, Umeton R (ed.) Machine Learning, Optimization, and Big Data. MOD 2017, Cham, Switzerland: Springer. MOD 2017 - The Third International Conference on Machine Learning, Optimization and Big Data, 14.9.2017 - 17.9.2017, Volterra, Italy, pp. 186-197. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-72926-8_16en_UK
dc.subjectOptimisationen_UK
dc.subjectMachine learningen_UK
dc.subjectEnsembleen_UK
dc.subjectBrain-computer interfaceen_UK
dc.subjectP300en_UK
dc.subjectEvolutionary computationen_UK
dc.subjectTransfer learningen_UK
dc.titleEvolving training sets for improved transfer learning in brain computer interfacesen_UK
dc.typeConference Paperen_UK
dc.identifier.doi10.1007/978-3-319-72926-8_16en_UK
dc.citation.issn0302-9743en_UK
dc.citation.spage186en_UK
dc.citation.epage197en_UK
dc.citation.publicationstatusPublisheden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-319-72926-8_16en_UK
dc.author.emailalexander.brownlee@stir.ac.uken_UK
dc.citation.btitleMachine Learning, Optimization, and Big Data. MOD 2017en_UK
dc.citation.conferencedates2017-09-14 - 2017-09-17en_UK
dc.citation.conferencelocationVolterra, Italyen_UK
dc.citation.conferencenameMOD 2017 - The Third International Conference on Machine Learning, Optimization and Big Dataen_UK
dc.citation.date21/12/2017en_UK
dc.citation.isbn978-3-319-72925-1en_UK
dc.citation.isbn978-3-319-72926-8en_UK
dc.publisher.addressCham, Switzerlanden_UK
dc.contributor.affiliationComputing Science and Mathematics - Divisionen_UK
dc.contributor.affiliationRobert Gordon Universityen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.isiWOS:000426151600016en_UK
dc.identifier.scopusid2-s2.0-85039452612en_UK
dc.identifier.wtid504453en_UK
dc.contributor.orcid0000-0003-4240-4161en_UK
dc.contributor.orcid0000-0001-7649-5669en_UK
dc.date.accepted2017-07-15en_UK
dc.description.refREF Compliant by Deposit in Stirling's Repositoryen_UK
dc.date.filedepositdate2018-01-19en_UK
dc.relation.funderprojectDAASE: Dynamic Adaptive Automated Software Engineeringen_UK
dc.relation.funderrefEP/J017515/1en_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

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