Web of Science: 31 cites, Scopus: 34 cites, Google Scholar: cites
Individually adapted imagery improves brain-computer interface performance in end-users with disability
Scherer, Reinhold (Institute for Knowledge Discovery (Graz, Àustria))
Faller, Josef (Institute for Knowledge Discovery (Graz, Àustria))
Friedrich, Elisabeth V.C. (Institute for Knowledge Discovery (Graz, Àustria))
Opisso, Eloy (Institut Germans Trias i Pujol. Institut Guttmann)
Costa Boned, Úrsula (Institut Germans Trias i Pujol. Institut Guttmann)
Kübler, Andrea (University of Würzburg. Institute of Psychology (Alemanya))
Müller-Putz, Gernot R. (Institute for Knowledge Discovery (Graz, Àustria))

Data: 2015
Resum: Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e. g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e. g. mental subtraction and mental word association) and "dynamic imagery" (e. g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.
Ajuts: European Commission 247447
European Commission 287774
European Commission 288566
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Electroencephalography ; Man-computer interface ; Central nervous system ; Spinal cord injury ; Stroke ; Linear discriminant analysis ; Machine learning ; Psychometrics
Publicat a: PloS one, Vol. 10, Núm. 5 (May 2015) , p. e0123727, ISSN 1932-6203

DOI: 10.1371/journal.pone.0123727
PMID: 25992718

14 p, 2.3 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
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 Registre creat el 2016-07-04, darrera modificació el 2022-03-26

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