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Title: Design considerations for long term non-invasive brain computer interface training with tetraplegic CYBATHLON pilot
Authors: Robinson, Neethu
Chouhan, Tushar
Mihelj, Ernest
Kratka, Paulina
Debraine, Frédéric
Wenderoth, Nicole
Guan, Cuntai
Lehner, Rea
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Robinson, 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-.
Journal: Frontiers in Human Neuroscience
Abstract: Several 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.
ISSN: 1662-5161
DOI: 10.3389/fnhum.2021.648275
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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