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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23825
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dc.contributor.authorOrset, Bastien-
dc.contributor.authorLee, Kyuhwa-
dc.contributor.authorChavarriaga, Ricardo-
dc.contributor.authorMillán, José del. R-
dc.date.accessioned2021-12-22T14:17:58Z-
dc.date.available2021-12-22T14:17:58Z-
dc.date.issued2021-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23825-
dc.description.abstractCurrent non-invasive Brain Machine interfaces commonly rely on the decoding of sustained motor imagery activity (MI). This approach enables a user to control brain-actuated devices by triggering predetermined motor actions. One major drawback of such strategy is that users are not trained to stop their actions. Indeed, the termination process involved in BMI is poorly understood with most of the studies assuming that the end of an MI action is similar to the resting state. Here we hypothesize that the process of stopping MI (MI termination) and resting state are two different processes that should be decoded independently due to the exhibition of different neural pattens. We compared the detection of both states transitions of an imagined movement, i.e. rest-to-movement (onset) and movement-to-rest (offset). Our results shows that both decoders show significant differences in term of performances and latency (N=17 Subjects) with the offset decoder able to detect faster and better MI termination. While studying this difference, we found that the offset decoder is primarily based on the use of features in Beta band which appears earlier. Based on this finding, we also proposed a Random Forest based decoder which enable to distinguish three classes (MI, MI termination and REST).de_CH
dc.format.extent17de_CH
dc.language.isoende_CH
dc.publisherbioRxivde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleStopping vs Resting state during motor imagery paradigmde_CH
dc.typeWorking Paper – Gutachten – Studiede_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
dc.identifier.doi10.1101/2021.06.15.448360de_CH
dc.identifier.doi10.21256/zhaw-23825-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Orset, B., Lee, K., Chavarriaga, R., & Millán, J. del. R. (2021). Stopping vs Resting state during motor imagery paradigm. bioRxiv. https://doi.org/10.1101/2021.06.15.448360
Orset, B. et al. (2021) Stopping vs Resting state during motor imagery paradigm. bioRxiv. Available at: https://doi.org/10.1101/2021.06.15.448360.
B. Orset, K. Lee, R. Chavarriaga, and J. del. R. Millán, “Stopping vs Resting state during motor imagery paradigm,” bioRxiv, 2021. doi: 10.1101/2021.06.15.448360.
ORSET, Bastien, Kyuhwa LEE, Ricardo CHAVARRIAGA und José del. R MILLÁN, 2021. Stopping vs Resting state during motor imagery paradigm. bioRxiv
Orset, Bastien, Kyuhwa Lee, Ricardo Chavarriaga, and José del. R Millán. 2021. “Stopping Vs Resting State during Motor Imagery Paradigm.” bioRxiv. https://doi.org/10.1101/2021.06.15.448360.
Orset, Bastien, et al. Stopping Vs Resting State during Motor Imagery Paradigm. bioRxiv, 2021, https://doi.org/10.1101/2021.06.15.448360.


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