Title
Towards A Brain-Computer Interface Based On Unsupervised Methods To Command A Lower-Limb Robotic Exoskeleton
Abstract
This work presents a brain-computer interface (BCI) based on unsupervised methods for conveying control commands to a robotic exoskeleton, in order to provide support to patients with severe motor disability during walking. For this purpose, an adaptive spatial filter based on similarity indices is proposed to preserve the useful information on electroencephalography (EEG) signals. Additionally, a method for feature selection based on the Maximal Information Compression Index (MICI), and the representation entropy (RE) is used, increasing its robustness for uncertain patterns, such as gait planning. Good values of accuracy (ACC >= 75%) and false positive rate (FPR <= 10%) were obtained for four subjects. Thus, this BCI based on unsupervised method may be suitable to recognize uncertainty pattern, such as gait planning.
Year
DOI
Venue
2018
10.1109/SMC.2018.00194
2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Keywords
Field
DocType
brain-computer interface, feature selection, gait planning, gait intention, spatial filter
False positive rate,Feature selection,Pattern recognition,Computer science,Brain–computer interface,Robustness (computer science),Artificial intelligence,Powered exoskeleton,Gait planning,Electroencephalography,Machine learning,Spatial filter
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
0
3