| Revue: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560772 |
| ISSN: | 1405-5546 |
| Autores: | Barajas Montiel, Sandra Eugenia1 Morales, Eduardo F.1 Escalante, Hugo Jair1 Reyes García, Carlos Alberto1 |
| Instituciones: | 1Instituto Nacional de Astrofísica Óptica y Electrónica, Coordinación de Ciencias Computacionales, México |
| Año: | 2023 |
| Periodo: | Ene-Mar |
| Volumen: | 27 |
| Número: | 1 |
| Paginación: | 211-221 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | The present work explores six different Multi-view learning (MVL) techniques for the classification of electroencephalogram (EEG) signals in order to take advantage of complementary descriptive information from different representations of the same object. We worked with four views of EEG signals extracted by applying two different feature extraction methods in time domain and two in the frequency domain. We propose a model for automatic selection of view combination, using the total number of views, then three views and finally two views with each MVL approach explored, based on classification performance. The classification accuracy achieved by the Multi-view learning approach and the subset of views selected by our model exceeds the results achieved in single view works where the same databases are used for pattern recognition in EEG signals. |
| Keyword: | Multi-view learning, EEG signal, Time domain, Frequency domain, Automatic selection |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
