Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80459
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dc.contributor.authorPrasad, Vinod Achutavarrieren
dc.contributor.authorZaidi, Ali Danishen
dc.contributor.authorRobinson, Neethuen
dc.contributor.authorRana, Mohiten
dc.contributor.authorGuan, Cuntaien
dc.contributor.authorBirbaumer, Nielsen
dc.contributor.authorSitaram, Ranganathaen
dc.contributor.editorVasilaki, Elenien
dc.date.accessioned2018-11-05T08:37:04Zen
dc.date.accessioned2019-12-06T13:49:59Z-
dc.date.available2018-11-05T08:37:04Zen
dc.date.available2019-12-06T13:49:59Z-
dc.date.issued2016en
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-. doi:10.1371/journal.pone.0159959en
dc.identifier.urihttps://hdl.handle.net/10356/80459-
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 multi-channel 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 real-time 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.en
dc.format.extent21 p.en
dc.language.isoenen
dc.relation.ispartofseriesPLOS ONEen
dc.rights© 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.en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.subjectOvert and Covert Movementsen
dc.subjectfNIRS Signalsen
dc.titleReal-time subject-independent pattern classification of overt and covert movements from fNIRS signalsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.identifier.doi10.1371/journal.pone.0159959en
dc.description.versionPublished versionen
dc.identifier.pmid27467528-
item.fulltextWith Fulltext-
item.grantfulltextopen-
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