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Title: Real-time subject-independent pattern classification of overt and covert movements from fNIRS signals
Authors: Robinson N.
Zaidi A.D.
Rana M.
Prasad V.A.
Guan C. 
Birbaumer N.
Sitaram R.
Keywords: brain computer interface
brain function
experimental model
feasibility study
hand movement
human experiment
motor cortex
support vector machine
brain computer interface
movement (physiology)
near infrared spectroscopy
Biofeedback, Psychology
Brain-Computer Interfaces
Spectroscopy, Near-Infrared
Issue Date: 2016
Citation: Robinson 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.
Rights: Attribution 4.0 International
Abstract: Recently, 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.
Source Title: PLoS ONE
ISSN: 19326203
DOI: 10.1371/journal.pone.0159959
Rights: Attribution 4.0 International
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