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Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique that recently has been used to measure changes in cerebral blood oxygenation associated with brain activity. Numbers of research groups have applied general linear model (GLM) based method to analyze the NIRS data. However, classical GLM based method cannot provide on-line analysis. Therefore, its usage is constrained in processing...
Near infrared spectroscopy (NIRS) is an effective technique for examining functional brain activity during cognitive tasks by enabling the measurement of the concentration changes of oxy-hemoglobin and deoxy-hemoglobin. In NIRS data analysis, accurate estimation of the hemodynamic response function (HRF) is still under investigation. Most existing methods assume that the shape of the HRF to be known...
The nonstationary nature of the brain signals provides a rather unstable input resulting in uncertainty and complexity in the control. Intelligent processing algorithms adapted to the task are a prerequisite for reliable BCI applications. This work presents a novel intelligent processing strategy for the realization of an effective BCI which has the capability to improved classification accuracy and...
Individual neurons can exhibit irregular behavior, whereas ensembles of different neurons might synchronize in order to process biological information or to produce regular, rhythmical activity. Therefore, the study of synchronization processes for populations of interacting neurons is basic to the understanding of some key issues in neuroscience. In the present study, we first investigated dynamic...
Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique that recently has been used to measure changes in cerebral blood oxygenation associated with brain activity. To date, there is no standard method for analyzing NIRS data, especially for real-time brain imaging applications. In this work, a novel real-time NIRS signal analysis framework based on the general linear model (GLM)...
This paper presents a method to estimate the signal strength of the functional near infrared spectroscopy (fNIRS) as parameters of orthogonalized form of general linear model (GLM). The idea is to convert the basis function (design matrix) or explanatory variables of a GLM into orthogonal functions using the usual Gram-Schmidt orthogo-nalization procedure. The coefficients associated with the orthogonal...
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