Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154060
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dc.contributor.authorRobinson, Neethuen_US
dc.contributor.authorChouhan, Tusharen_US
dc.contributor.authorMihelj, Ernesten_US
dc.contributor.authorKratka, Paulinaen_US
dc.contributor.authorDebraine, Frédéricen_US
dc.contributor.authorWenderoth, Nicoleen_US
dc.contributor.authorGuan, Cuntaien_US
dc.contributor.authorLehner, Reaen_US
dc.date.accessioned2022-06-08T02:42:25Z-
dc.date.available2022-06-08T02:42:25Z-
dc.date.issued2021-
dc.identifier.citationRobinson, N., Chouhan, T., Mihelj, E., Kratka, P., Debraine, F., Wenderoth, N., Guan, C. & Lehner, R. (2021). Design considerations for long term non-invasive brain computer interface training with tetraplegic CYBATHLON pilot. Frontiers in Human Neuroscience, 15, 648275-. https://dx.doi.org/10.3389/fnhum.2021.648275en_US
dc.identifier.issn1662-5161en_US
dc.identifier.urihttps://hdl.handle.net/10356/154060-
dc.description.abstractSeveral studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relation.ispartofFrontiers in Human Neuroscienceen_US
dc.rights© 2021 Robinson, Chouhan, Mihelj, Kratka, Debraine,Wenderoth, Guan and Lehner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleDesign considerations for long term non-invasive brain computer interface training with tetraplegic CYBATHLON piloten_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.3389/fnhum.2021.648275-
dc.description.versionPublished versionen_US
dc.identifier.pmid34211380-
dc.identifier.scopus2-s2.0-85111948518-
dc.identifier.volume15en_US
dc.identifier.spage648275en_US
dc.subject.keywordsTetraplegiaen_US
dc.subject.keywordsBrain Computer Interfaceen_US
dc.description.acknowledgementThis work was supported by the National Natural Science Foundation of China (Grants 81970886, 81570915, and 81870723) and the National Basic Research Program of China (Grant 2011CB504506).en_US
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