- title = "Human mental states classification using {EEG} by means of Genetic Programming",
- titletranslation = "Classification de l'{\'e}tat mental humain par programmation g{\'e}n{\'e}tique sur des signaux EEG",
- author = "Emigdio {Z.Flores Lopez}",
- school = "Instituto tecnologico de Tijuana",
- year = "2017",
- type = "Doctor in Engineering Sciences",
- address = "Tijuana, Baja California, Mexico",
- month = jul # "~05",
- keywords = "genetic algorithms, genetic programming, EEG, feature extraction, holderian regularity, matching pursuit, regression, classification",
- bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
- contributor = "Quality control and dynamic reliability and Leonardo Trujillo and Pierrick Legrand",
- identifier = "tel-01668672",
- language = "en",
- oai = "oai:HAL:tel-01668672v1",
- rights = "info:eu-repo/semantics/OpenAccess",
-
URL = "
https://hal.inria.fr/tel-01668672",
-
URL = "https://hal.inria.fr/tel-01668672/document",
-
URL = "
https://hal.inria.fr/tel-01668672/file/PhD_Thesis_Emigdio.pdf",
- size = "354 pages",
- abstract = "The advances in the development of Brain-Computer Interfaces(BCI) have been increasing in recent years, mostly because the level of convergence from multi-disciplinary techniques has evolved. The electroencephalography(EEG), a brain recording method studied in this thesis, allows the construction of BCIs, however the signals are rather complex to process, which requires methodologies that efficiently extract patterns from them. This thesis explores two directions: first, a system is proposed for the epilepsy seizures recognition using a combination of signal processing methods for an efficient feature extraction; second,it explores the usage of a meta-heuristic algorithm, namely Genetic Programming (GP), as an alternative in the design of BCIs. Nonetheless,there is currently open-issues in GP that this thesis also explores: is there a more efficient search methodology in the exploration by GP?; what is a proper representation depending on the studied problem?; which are the most adequate search operators?. For the first topic, a thoroughly study is presented by introducing a memetic GP applied to regression problems. Then, it is extended by adapting it to classification problems. The results are positive; GP is greatly benefited from the combination of a general and a Local Search (LS) methodology. The last two topics are studied simultaneously in the development of a recognition system for mental states using EEG. A GP version (+FEGP) is proposed that evolves feature extraction models by using specialized search operators,individuals representation and fitness function. The results show that the combination of these reaches a state-of-the-art accuracy for the particular task of mental states recognition.",
- abstract = "Los avances en el desarrollo de Interfaces Cerebro-Computadora (BCI, porsus siglas en ingles Brain-Computer Interface) se han incrementado en anos recientes,principalmente porque ha evolucionado el nivel de convergencia detecnicas multidisciplinarias...........",
-
notes = "Supervisors: Leonardo Trujillo and Pierrick
Legrand
Also known as \cite{phd/hal/ZFlores17} \cite{oai:HAL:tel-01668672v1}",
