EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
| hal.structure.identifier | Instituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana] | |
| dc.contributor.author | Z-FLORES, Emigdio | |
| hal.structure.identifier | Instituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana] | |
| dc.contributor.author | TRUJILLO, Leonardo | |
| hal.structure.identifier | Quality control and dynamic reliability [CQFD] | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| hal.structure.identifier | Université de Bordeaux [UB] | |
| dc.contributor.author | LEGRAND, Pierrick | |
| hal.structure.identifier | Université de Bordeaux [UB] | |
| dc.contributor.author | FAÏTA-AÏNSEBA, Frédérique | |
| dc.date.accessioned | 2024-04-04T02:49:41Z | |
| dc.date.available | 2024-04-04T02:49:41Z | |
| dc.date.issued | 2020-09 | |
| dc.identifier.issn | 1999-4893 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/191843 | |
| dc.description.abstractEn | The design of efficient electroencephalogram (EEG) classification systems for the detectionof mental states is still an open problem. Such systems can be used to provide assistance to humansin tasks where a certain level of alertness is required, like in surgery or in the operation of heavymachines, among others. In this work, we extend a previous study where a classification system isproposed using a Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for theclassification of two mental states, namely a relaxed and a normal state. Here, we propose an enhancedfeature extraction algorithm (Augmented Feature Extraction with Genetic Programming, or+FEGP)that improves upon previous results by employing a Genetic-Programming-based methodologyon top of the CSP. The proposed algorithm searches for non-linear transformations that build newfeatures and simplify the classification task. Although the proposed algorithm can be coupled withany classifier, LDA achieves 78.8% accuracy, the best predictive accuracy among tested classifiers,significantly improving upon previously published results on the same real-world dataset. | |
| dc.language.iso | en | |
| dc.publisher | MDPI | |
| dc.subject.en | EEG | |
| dc.subject.en | Classification | |
| dc.subject.en | Genetic programming | |
| dc.subject.en | Feature extraction | |
| dc.subject.en | Mental states | |
| dc.title.en | EEG Feature Extraction Using Genetic Programming for the Classification of Mental States | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.3390/a13090221 | |
| dc.subject.hal | Informatique [cs]/Intelligence artificielle [cs.AI] | |
| dc.description.sponsorshipEurope | Analysis and classification of mental states of vigilance with evolutionary computation | |
| bordeaux.journal | Algorithms | |
| bordeaux.page | 221 | |
| bordeaux.volume | 13 | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.issue | 9 | |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-02943474 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-02943474v1 | |
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