Classification of EEG signals using LDA and QDA applied to a Brain Interface - Computer based on P300

• Autores: Cabezas, Franklin Alfredo, Cabezas Soldevilla, Fermín Rafael
• Formato: artículo
• Fecha de Publicación: 2018
• Institución: Universidad Nacional de Ingeniería
• Repositorio: Revista UNI - Tecnia
• Lenguaje: español
• OAI Identifier: oai:oai:revistas.uni.edu.pe:article/573
• Enlace del recurso: http://www.revistas.uni.edu.pe/index.php/tecnia/article/view/573
• Nivel de acceso: acceso abierto
• Materia: P300; Machine Learning; Interface cerebro –Computador; Enfermedades Neurodegenerativas

Different Machine Learning techniques have been used in order to identify the wishes of patients with neurodegenerative diseases. For this purpose, a database of electroencephalographic (EEG) signals was used, which were filtered and processed. The determination of the wills of patients was achieved through the identification of brain waves P300, these signals are presented in the brain in response to an unexpected stimulus and among its many applications is the implementation of the so-called Brain-Computer Interface .