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- W2801587739 endingPage "e2003787" @default.
- W2801587739 startingPage "e2003787" @default.
- W2801587739 abstract "This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain–computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized that, contrary to the popular trend of focusing mostly on the machine learning aspects of MI BCI training, a comprehensive mutual learning methodology that reinstates the three learning pillars (at the machine, subject, and application level) as equally significant could lead to a BCI–user symbiotic system able to succeed in real-world scenarios such as the Cybathlon event. Two severely impaired participants with chronic spinal cord injury (SCI), were trained following our mutual learning approach to control their avatar in a virtual BCI race game. The competition outcomes substantiate the effectiveness of this type of training. Most importantly, the present study is one among very few to provide multifaceted evidence on the efficacy of subject learning during BCI training. Learning correlates could be derived at all levels of the interface—application, BCI output, and electroencephalography (EEG) neuroimaging—with two end-users, sufficiently longitudinal evaluation, and, importantly, under real-world and even adverse conditions." @default.
- W2801587739 created "2018-05-17" @default.
- W2801587739 creator A5010732625 @default.
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- W2801587739 creator A5068424294 @default.
- W2801587739 creator A5080036634 @default.
- W2801587739 creator A5090275731 @default.
- W2801587739 date "2018-05-10" @default.
- W2801587739 modified "2023-10-06" @default.
- W2801587739 title "The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users" @default.
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- W2801587739 doi "https://doi.org/10.1371/journal.pbio.2003787" @default.
- W2801587739 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5944920" @default.
- W2801587739 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29746465" @default.
- W2801587739 hasPublicationYear "2018" @default.
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