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.
11The "initial-dip" is a transient decrease frequently observed in functional neuroimaging signals, immediately after 12 stimulus onset, and is believed to originate from a rise in deoxy-hemoglobin (HbR) caused by local neural activity. It has been 13 shown to be more spatially specific than the hemodynamic response, and is believed to represent focal neuronal activity. 14 However, despite being observed in various neuroimaging modalities (such as fMRI, fNIRS, etc), its origins are disputed and 15 its neuronal correlates unknown. Here, we show that the initial-dip is dominated by a decrease in total-hemoglobin (HbT). 16We also find a biphasic response in HbR, with an early decrease and later rebound. However, HbT decreases were always large 17 enough to counter spiking-induced increases in HbR. Moreover, the HbT-dip and HbR-rebound were strongly coupled to 18 highly localized spiking activity. Our results suggest that the HbT-dip helps prevent accumulation of spiking-induced HbR-19 concentration in capillaries by flushing out HbT, probably by active venule dilation. 20 functional neuroimaging modalities (such as BOLD-fMRI [2], [7], [9], optical imaging [10], [11], fNIRS[12] and pO2-measurements
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