Classifying BCI signals from novice users with Extreme Learning Machine
EstadísticasVer Estadísticas de uso
MetadatosMostrar el registro completo del ítem
Disciplina/sIngeniería, Industria y Construcción
Volume 15, Issue 1 Previous ArticleNext Article Classifying BCI signals from novice users with extreme learning machine Germán Rodríguez-Bermúdez / Andrés Bueno-Crespo / F. José Martinez-Albaladejo Published Online: 2017-07-07 | DOI: https://doi.org/10.1515/phys-2017-0056 OPEN ACCESS DOWNLOAD PDF Abstract Brain computer interface (BCI) allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM) has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.