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Improving EEG-BCI analysis for low certainty subjects by using dictionary learning
Victorino, J.; Noirhomme, Quentin; Lule, D. et al.
2015In 2015 20th Symposium on Signal Processing, Images and Computer Vision, STSIVA 2015 - Conference Proceedings
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Keywords :
Computer vision; Diagnosis; Electroencephalography; Electrophysiology; Image processing; Interfaces (computer); Medical computing; Signal processing; Brain response; Clinical routine; Command following; Dictionary learning; Improve performance; Intermediate representations; Sparse dictionaries; Weighted averages; Brain computer interface
Abstract :
[en] The diagnosis of patients with Disorders Of Consciousness represents a challenge in the clinical routine. Recently, Brain Computer Interfaces based in Electroencephalography (EEG-BCI) have been used to detect signs of consciousness in these patients. This approach allows to discover brain responses to command following. Nevertheless, a reliable BCI strategy must to be able to determine the commands with high levels of certainty. Current results reported in the literature evidence that about 25% of the subjects in which BCI is used may have low performances, near to the chance level, even in collaborating subjects. In this work, we propose a novel approach based on dictionary learning representations aimed to improve performance in low certainty subjects. We propose to introduce an intermediate representation scheme, based on sparse dictionaries, before feature selection step. Our main assumption is that by using these representations we can capture more efficiently the EEG signal structure for subjects responses. The results show that using the new representation the weighted average performance in command following outcome the previews proposed methods of 64.8% to 67.9%. Higher improvements in performance were obtained for low certainly subjects. Our results suggests that this approach may improve BCI-EEG performance in low certainly subjects. © 2015 IEEE.
Disciplines :
Neurology
Author, co-author :
Victorino, J.;  Universidad Central, Bogotá, Colombia
Noirhomme, Quentin ;  Université de Liège - ULiège > GIGA CRC In vivo Imaging
Lule, D.;  Coma Science Group, Cyclotron Research Centre, University Hospital of Liège, Belgium, Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Germany, Department of Psychology i, University of Würzburg, Germany, Department of Neurology, University of Ulm, Germany
Kleih, S. C.;  Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Germany
Chatelle, Camille ;  Université de Liège - ULiège > Consciousness-Coma Science Group
Halder, S.;  Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Germany
Demertzi, Athina  ;  Université de Liège - ULiège > Consciousness-Physiology of Cognition
Bruno, M.-A.;  Coma Science Group, Cyclotron Research Centre, University Hospital of Liège, Belgium
Gosseries, Olivia  ;  Université de Liège - ULiège > Consciousness-Coma Science Group
VANHAUDENHUYSE, Audrey  
Schnakers, Caroline;  Coma Science Group, Cyclotron Research Centre, University Hospital of Liège, Belgium
Thonnard, M
Soddu, A.;  Physics and Astronomy Dept. Western University, London, Canada
Kübler, A.;  Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Germany
Laureys, Steven  ;  Université de Liège - ULiège > Consciousness-Coma Science Group
Gómez, F.;  Universidad Central, Bogotá, Colombia
Guarin, P. V.
Posada, L. G.
More authors (8 more) Less
Title :
Improving EEG-BCI analysis for low certainty subjects by using dictionary learning
Publication date :
2015
Event name :
20th Symposium on Signal Processing, Images and Computer Vision, STSIVA 2015
Event date :
2 September 2015 through 4 September 2015
Main work title :
2015 20th Symposium on Signal Processing, Images and Computer Vision, STSIVA 2015 - Conference Proceedings
Publisher :
Institute of Electrical and Electronics Engineers Inc.
Peer reviewed :
Peer reviewed
Funders :
PUJ - Pontificia Universidad Javeriana [CO]
Commentary :
118227 9781467394611
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