Treffer: Endogenous brain―machine interface based on the correlation of EEG maps

Title:
Endogenous brain―machine interface based on the correlation of EEG maps
Authors:
UBEDA, Andrés, IANEZ, Eduardo, AZORIN, José M, PEREZ-VIDAL, Carlos
Source:
Computer Assisted Tools for Medical RoboticsComputer methods and programs in biomedicine (Print). 112(2):302-308
Publisher Information:
Kidlington: Elsevier, 2013.
Publication Year:
2013
Physical Description:
print, 21 ref
Original Material:
INIST-CNRS
Subject Terms:
Biomedical engineering, Génie biomédical, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Sciences biologiques et medicales, Biological and medical sciences, Sciences biologiques fondamentales et appliquees. Psychologie, Fundamental and applied biological sciences. Psychology, Psychologie. Psychophysiologie, Psychology. Psychophysiology, Cognition. Intelligence, Imagerie mentale. Représentation mentale, Mental imagery. Mental representation, Sciences medicales, Medical sciences, Neurologie, Neurology, Techniques d'exploration et de diagnostic (generalites), Investigative techniques, diagnostic techniques (general aspects), Electrodiagnostic. Enregistrement des activités électriques, Electrodiagnosis. Electric activity recording, Système nerveux, Nervous system, Psychologie. Psychanalyse. Psychiatrie, Psychology. Psychoanalysis. Psychiatry, Asservissement visuel, Visual servoing, Servomando visual, Classification, Clasificación, Corrélation, Correlation, Correlación, Dialogue homme machine, Man machine dialogue, Diálogo hombre máquina, Electroencéphalographie, Electroencephalography, Electroencefalografía, Formation utilisateur, User training, Formación usuario, Handicap physique, Physical handicap, Deficiencia física, Imagerie motrice, Motor imagery, Imaginería motriz, Interface graphique, Graphical interface, Interfaz grafica, Interface utilisateur, User interface, Interfase usuario, Méthode non invasive, Non invasive method, Método no invasivo, Stabilité, Stability, Estabilidad, Système nerveux central, Central nervous system, Sistema nervioso central, Temps réel, Real time, Tiempo real, Volontariat, Volunteering, Voluntariado, Brain―machine interface, EEG mapping, Real-time application
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Biomedical Neuroengineering Group (nBio), Miguel Hernández University of Elche, Avda. de la Universidad S/N, Ed. Quorum V, 03202, Elche, Spain
ISSN:
0169-2607
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=27805637
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Neurology

Psychology. Ethology

Scanning and diagnostic techniques (generalities)

FRANCIS
Accession Number:
edscal.27805637
Database:
PASCAL Archive

Weitere Informationen

In this paper, a non-invasive endogenous brain―machine interface (BMI) based on the correlation of EEG maps has been developed to work in real-time applications. The classifier is able to detect two mental tasks related to motor imagery with good success rates and stability. The BMI has been tested with four able-bodied volunteers. First, the users performed a training with visual feedback to adjust the classifier. Afterwards, the users carried out several trajectories in a visual interface controlling the cursor position with the BMI. In these tests, score and accuracy were measured. The results showed that the participants were able to follow the targets during the performed trajectory, proving that the EEG mapping correlation classifier is ready to work in more complex real-time applications aimed at helping people with a severe disability in their daily life.