Android Feedback-Based Training Modulates Sensorimotor Rhythms During Motor Imagery.
IEEE Trans Neural Syst Rehabil Eng
; 26(3): 666-674, 2018 03.
Article
em En
| MEDLINE
| ID: mdl-29522410
EEG-based brain computer interface (BCI) systems have demonstrated potential to assist patients with devastating motor paralysis conditions. However, there is great interest in shifting the BCI trend toward applications aimed at healthy users. Although BCI operation depends on technological factors (i.e., EEG pattern classification algorithm) and human factors (i.e., how well the person can generate good quality EEG patterns), it is the latter that is least investigated. In order to control a motor imagery-based BCI, users need to learn to modulate their sensorimotor brain rhythms by practicing motor imagery using a classical training protocol with an abstract visual feedback. In this paper, we investigate a different BCI training protocol using a human-like android robot (Geminoid HI-2) to provide realistic visual feedback. The proposed training protocol addresses deficiencies of the classical approach and takes the advantage of body-abled user capabilities. Experimental results suggest that android feedback-based BCI training improves the modulation of sensorimotor rhythms during motor imagery task. Moreover, we discuss how the influence of body ownership transfer illusion toward the android might have an effect on the modulation of event-related desynchronization/synchronization activity.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Retroalimentação Sensorial
/
Interfaces Cérebro-Computador
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Imaginação
Limite:
Adult
/
Female
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Humans
/
Male
Idioma:
En
Revista:
IEEE Trans Neural Syst Rehabil Eng
Assunto da revista:
ENGENHARIA BIOMEDICA
/
REABILITACAO
Ano de publicação:
2018
Tipo de documento:
Article