Publicación

Autores
Calvo Castro Francisco Hiram
Paredes Paredes José Luis
Figueroa Nazuno Jesús Guillermo
Título Measuring Concept Semantic Relatedness through Common Spatial Pattern Feature Extraction on EEG Signals
Tipo Revista
Sub-tipo Indefinido
Descripción Cognitive Systems Research
Resumen We study the semantic relationship between pairs of nouns of concrete objects such as “HORSE - SHEEP” and “SWING - MELON” and how this relationship activity is reflected in EEG signals. We collected 18 sets of EEG records; each set containing 150 events of stimulation. In this work we focus on feature extraction algorithms. Particularly, we highlight Common Spatial Pattern (CSP) as a method of feature extraction. Based on these latter, different classifiers were trained in order to associate a set of signals to a previously learned human answer, pertaining to two classes: semantically related, or not semantically related. The results of classification accuracy were evaluated comparing with other four methods of feature extraction, and using classification algorithms from five different families. In all cases, classification accuracy was benefited from using CSP instead of FDTW, LPC, PCA or ICA for feature extraction. Particularly with the combination CSP-Naïve Bayes we obtained the best average precision of 84.63%
Observaciones https://www.sciencedirect.com/science/article/pii/S1389041717302267?via%3Dihub http://dx.doi.org/10.1016/j.cogsys.2018.03.004
Lugar
País Mexico
No. de páginas 36-51
Vol. / Cap. 50
Inicio 2018-03-24
Fin
ISBN/ISSN 1064-1246