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  Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals

Robinson, N., Zaidi, A., Rana, M., Prasad, V., Guan, C., Birbaumer, N., et al. (2016). Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals. PLoS ONE, 11(7), 1-21. doi:10.1371/journal.pone.0159959.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-79AC-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-8793-0
Genre: Journal Article

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Robinson, N, Author
Zaidi, AD1, 2, Author              
Rana, M, Author
Prasad, V, Author
Guan, C, Author
Birbaumer, N, Author
Sitaram, R1, 2, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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 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.

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 Dates: 2016-07
 Publication Status: Published online
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 Identifiers: DOI: 10.1371/journal.pone.0159959
eDoc: e0159959
BibTex Citekey: RobinsonZRPGBS2016
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Title: PLoS ONE
Source Genre: Journal
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Pages: - Volume / Issue: 11 (7) Sequence Number: - Start / End Page: 1 - 21 Identifier: -