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dc.contributor.authorKapralov, Nikolai
dc.contributor.authorIdaji, Mina Jamshidi
dc.contributor.authorStephani, Tilman
dc.contributor.authorStudenova, Alina
dc.contributor.authorVidaurre, Carmen
dc.contributor.authorRos, Tomas
dc.contributor.authorVillringer, Arno
dc.contributor.authorNikulim, Vadim
dc.date.accessioned2024-10-23T08:33:50Z
dc.date.available2024-10-23T08:33:50Z
dc.date.issued2024
dc.identifier.citationKapralov, N., Idaji, M.J., Stephani, T., Studenova, N., Vidaurre, C., Ros, T., Villringer, A., & Nikulin, V. (2024). Sensorimotor brain–computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data. Journal Of Neural Engineering, 21:056027. Doi:10.1088/1741-2552/ad7a24es_ES
dc.identifier.citationJournal of Neural Engineering
dc.identifier.issn1741-2560
dc.identifier.urihttp://hdl.handle.net/10810/70061
dc.descriptionPublished on 8 october 2024es_ES
dc.description.abstractObjective. Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain–computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user’s brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user’s BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results. Approach. To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline. Main results. Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR. Significance. Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user’s BCI performance.es_ES
dc.description.sponsorshipWe would like to thank the authors of the dataset for making it publicly available and James Stieger in particular for providing additional information about the dataset on request. C V was funded by the Spanish Ministry of Science, Innovation and Universities, Reference Number PID2020-118829RBI00. T R was supported by Swiss National Science Foundation (SNSF), grant number 215712.es_ES
dc.language.isoenges_ES
dc.publisherIOP Publishinges_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/PID2020-118829RB-I00es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectElectroencephalographyes_ES
dc.subjectbrain computer interfacees_ES
dc.subjectmotor imageryes_ES
dc.subjectsource space analysises_ES
dc.subjectfunctional connetivityes_ES
dc.subjectmultiverse analysises_ES
dc.subjectlongitudinal dataes_ES
dc.titleSensorimotor brain–computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holderOriginal Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.es_ES
dc.relation.publisherversionhttps://iopscience.iop.org/journal/1741-2552es_ES
dc.identifier.doi10.1088/1741-2552/ad7a24


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