Table 2.
Signal processing methodologies for extraction of evoked-hemodynamic response.
| References | Methodological details |
|---|---|
| Jobsis, 1977 | Possibility to detect changes of cortical oxygen using NIR light. |
| Cope and Delpy, 1988 | Design of NIR system with four wavelengths (778, 813, 867, and 904 nm) with applying modified Beer-Lambert law for data conversion. |
| Friston et al., 1994 | Statistical parameter mapping software for fMRI but later used for fNIRS data analysis with modifications. |
| Boynton et al., 1996 | HRF model with one Gamma function with two free parameters. |
| Prince et al., 2003 | Biological signals modeled as sum of sinusoids. |
| Jasdzewski et al., 2003 | Impulse response, initial dip, and time to peak analysis in fNIRS signal. |
| Koh et al., 2007 | A software functional optical signal analysis (FOSA) was introduced based on GLM methodology. |
| Plichta et al., 2006, 2007 | GLM methodology with ordinary least square estimation to generate functional maps of visual cortex. |
| Taga et al., 2007 | Analysis of effect of source-detector separation to fNIRS hemodynamic response. |
| Koray et al., 2008 | Estimation of constrained HRF parameters in Bayesian frame work. |
| Abdelnour and Huppert, 2009 | GLM based methodology with Kalman filter to estimate handedness. |
| Ye et al., 2009 | GLM based NIRS-SPM software package for analysis of fNIRS data. |
| Hu et al., 2010 | Brain functional maps by using GLM and Kalman filtering. |
| Zhang et al., 2011b | Recursive least squares (RLS)-empirical mode decomposition for noise reduction. |
| Zhang et al., 2012 | RLS estimation with forgetting factor to remove physiological noise. |
| Aqil et al., 2012a | GLM and RLSE for estimation of brain functional maps. |
| Aqil et al., 2012b | Generation of cHRF using state-space approach. |
| Scarpa et al., 2013 | Reference channel based methodology for estimation of evoked-response |
| Santosa et al., 2013 | ICA methodology to estimate pre-defined cortical activation signal. |
| Kamran and Hong, 2014 | Linear parameter varying model and adaptive filtering to estimate HRF and functional maps of brain. |
| Barati et al., 2013 | Principle component analysis to continuous fNIRS data (using spline method). |
| Kamran and Hong, 2014 | Auto-regressive moving average model with exogenous signal (ARMAX) model for cortical activation estimation. |
| Hong and Nugyen, 2014 | State-space model for impulse response using fNIRS. |