Exploiting task constraints for self-calibrated brain-machine interface control using error-related potentials
Financiación FP7 / Fp7 Funds
Resumen: This paper presents a new approach for self-calibration BCI for reaching tasks using error- related potentials. The proposed method exploits task constraints to simultaneously cali- brate the decoder and control the device, by using a robust likelihood function and an ad- hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a pre- viously proposed BCI protocol for reaching tasks over a grid. The results show that it is pos- sible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach
Idioma: Inglés
DOI: 10.1371/journal.pone.0131491
Año: 2015
Publicado en: PLoS ONE 10, 7 (2015), [15 pp]
ISSN: 1932-6203

Factor impacto JCR: 3.057 (2015)
Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 11 / 62 = 0.177 (2015) - Q1 - T1
Factor impacto SCIMAGO:

Financiación: info:eu-repo/grantAgreement/ES/DGA/T04
Financiación: "info:eu-repo/grantAgreement/EC/FP7/291771/EU/""Experienced Researcher Fellowship Programme at the """"Ecole Polytechnique Fédérale de Lausanne (EPFL)""""""/EPFL Fellows"
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2011-25892
Tipo y forma: Article (Published version)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)

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