title: Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals.
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.
authors.affiliation: School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
authors.forename: Neethu
authors.initials: N
authors.lastname: Robinson
authors.affiliation: Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.|Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen, Germany.
authors.forename: Ali Danish
authors.identifier: http://orcid.org/0000-0001-8370-1774
authors.initials: AD
authors.lastname: Zaidi
authors.affiliation: Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen, Germany.
authors.forename: Mohit
authors.initials: M
authors.lastname: Rana
authors.affiliation: School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
authors.forename: Vinod A
authors.initials: VA
authors.lastname: Prasad
authors.affiliation: School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.|Department of Neural and Biomedical Technology, Institute for Infocomm Research, A*STAR, Singapore, Singapore.
authors.forename: Cuntai
authors.initials: C
authors.lastname: Guan
authors.affiliation: Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen, Germany.|Wyss Center for Bio and Neuroengineering, Geneva, Switzerland.
authors.forename: Niels
authors.initials: N
authors.lastname: Birbaumer
authors.affiliation: School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.|Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.|Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen, Germany.|Department of Psychiatry and Division of Neuroscience, Schools of Engineering, Biology & Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
authors.forename: Ranganatha
authors.initials: R
authors.lastname: Sitaram
mesh_terms: D001676:Biofeedback, Psychology; D062207:Brain-Computer Interfaces; D006801:Humans; D009068:Movement; D019265:Spectroscopy, Near-Infrared / Q000379:methods*