Robust classification of motor imagery EEG signals using statistical time-domain features.
Physiol Meas
; 34(11): 1563-79, 2013 Nov.
Article
en En
| MEDLINE
| ID: mdl-24152422
The tradeoff between computational complexity and speed, in addition to growing demands for real-time BMI (brain-machine interface) systems, expose the necessity of applying methods with least possible complexity. Willison amplitude (WAMP) and slope sign change (SSC) are two promising time-domain features only if the right threshold value is defined for them. To overcome the drawback of going through trial and error for the determination of a suitable threshold value, modified WAMP and modified SSC are proposed in this paper. Besides, a comprehensive assessment of statistical time-domain features in which their effectiveness is evaluated with a support vector machine (SVM) is presented. To ensure the accuracy of the results obtained by the SVM, the performance of each feature is reassessed with supervised fuzzy C-means. The general assessment shows that every subject had at least one of his performances near or greater than 80%. The obtained results prove that for BMI applications, in which a few errors can be tolerated, these combinations of feature-classifier are suitable. Moreover, features that could perform satisfactorily were selected for feature combination. Combinations of the selected features are evaluated with the SVM, and they could significantly improve the results, in some cases, up to full accuracy.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Señales Asistido por Computador
/
Electroencefalografía
/
Interfaces Cerebro-Computador
/
Actividad Motora
Límite:
Humans
Idioma:
En
Revista:
Physiol Meas
Asunto de la revista:
BIOFISICA
/
ENGENHARIA BIOMEDICA
/
FISIOLOGIA
Año:
2013
Tipo del documento:
Article
País de afiliación:
Malasia
Pais de publicación:
Reino Unido