Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0159959
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dc.titleReal-time subject-independent pattern classification of overt and covert movements from fNIRS signals
dc.contributor.authorRobinson N.
dc.contributor.authorZaidi A.D.
dc.contributor.authorRana M.
dc.contributor.authorPrasad V.A.
dc.contributor.authorGuan C.
dc.contributor.authorBirbaumer N.
dc.contributor.authorSitaram R.
dc.date.accessioned2019-11-06T07:48:28Z
dc.date.available2019-11-06T07:48:28Z
dc.date.issued2016
dc.identifier.citationRobinson N., Zaidi A.D., Rana M., Prasad V.A., Guan C., Birbaumer N., Sitaram R. (2016). Real-time subject-independent pattern classification of overt and covert movements from fNIRS signals. PLoS ONE 11 (7) : e0159959. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0159959
dc.identifier.issn19326203
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/161563
dc.description.abstractRecently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain-Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multichannel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based realtime subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity. © 2016 Robinson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20191101
dc.subjectbrain computer interface
dc.subjectbrain function
dc.subjectexperimental model
dc.subjectfeasibility study
dc.subjecthand movement
dc.subjecthemodynamics
dc.subjecthuman
dc.subjecthuman experiment
dc.subjectimagery
dc.subjectmotor cortex
dc.subjectneurofeedback
dc.subjectrehabilitation
dc.subjectsupport vector machine
dc.subjectbiofeedback
dc.subjectbrain computer interface
dc.subjectmovement (physiology)
dc.subjectnear infrared spectroscopy
dc.subjectprocedures
dc.subjectBiofeedback, Psychology
dc.subjectBrain-Computer Interfaces
dc.subjectHumans
dc.subjectMovement
dc.subjectSpectroscopy, Near-Infrared
dc.typeArticle
dc.contributor.departmentDEPT OF ELECTRICAL & COMPUTER ENGG
dc.description.doi10.1371/journal.pone.0159959
dc.description.sourcetitlePLoS ONE
dc.description.volume11
dc.description.issue7
dc.description.pagee0159959
dc.published.statePublished
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This item is licensed under a Creative Commons License Creative Commons