Web of Science: 15 citations, Scopus: 18 citations, Google Scholar: citations,
Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment
Faller, Josef (Graz University of Technology)
Scherer, Reinhold (Graz University of Technology)
Friedrich, Elisabeth V. C. (University of California. Cognitive Neuroscience Lab)
Costa, Ursula (Institut Germans Trias i Pujol. Institut Guttmann)
Opisso, Eloy (Institut Germans Trias i Pujol)
Medina, Josep (Institut Germans Trias i Pujol)
Müller-Putz, Gernot R. (Graz University of Technology)
Universitat Autònoma de Barcelona

Date: 2014
Abstract: Individuals with severe motor impairment can use event-related desynchronization (ERD) based BCIs as assistive technology. Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks (" SMR-AdBCI ") have proven effective for healthy users. We aim to find an improved configuration of such an adaptive ERD-based BCI for individuals with severe motor impairment as a result of spinal cord injury (SCI) or stroke. We hypothesized that an adaptive ERD-based BCI, that automatically selects a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration ("Auto-AdBCI") could allow for higher control performance than a conventional SMR-AdBCI. To answer this question we performed offline analyses on two sessions (21 data sets total) of cue-guided, five-class electroencephalography (EEG) data recorded from individuals with SCI or stroke. On data from the twelve individuals in Session 1, we first identified three bipolar derivations for the SMR-AdBCI. In a similar way, we determined three bipolar derivations and four mental tasks for the Auto-AdBCI. We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results. On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0. 01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI ; average accuracy of 75. 7 vs. 66. 3%).
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Language: Anglès
Document: article ; recerca ; publishedVersion
Subject: Adaptive brain-computer interface (BCI) ; Stroke ; Spinal cord injury (SCI) ; Event-related desynchronization (ERD) ; Electroencephalography (EEG) ; Assistive technology ; Mental tasks
Published in: Frontiers in Neuroscience, Vol. 8 (october 2014) , ISSN 1662-453X

DOI: 10.3389/fnins.2014.00320
PMID: 25368546

11 p, 3.7 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (scientific output) > Health sciences and biosciences > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
Articles > Research articles
Articles > Published articles

 Record created 2018-01-29, last modified 2020-11-07

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