Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction.
Annu Int Conf IEEE Eng Med Biol Soc
; 2013: 6341-4, 2013.
Article
en En
| MEDLINE
| ID: mdl-24111191
ABSTRACT
Recent studies have demonstrated decoding of lower extremity limb kinematics from noninvasive electroencephalography (EEG), showing feasibility for development of an EEG-based brain-machine interface (BMI) to restore mobility following paralysis. Here, we present a new technique that preserves the statistical richness of EEG data to classify movement state from time-embedded low frequency EEG signals. We tested this new classifier, using cross-validation procedures, during sit-to-stand and stand-to-sit activity in 10 subjects and found decoding accuracy of greater than 95% on average. These results suggest that this classification technique could be used in a BMI system that, when combined with a robotic exoskeleton, can restore functional movement to individuals with paralysis.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Electroencefalografía
/
Actividad Motora
Límite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2013
Tipo del documento:
Article