Compared with other existing functional imaging methods e.g., functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), Electroencephalography (EEG), and Magnetoencephalography (MEG), the advantages of DOI for studying brain function include good temporal resolution of the hemodynamic response, measurement of both HbO and HbR, nonionizing radiation, portability, and low cost. Disadvantages include modest spatial resolution and limited penetration depth.
The sensitivity of NIRS to evoked brain activity is also reduced by systemic physiological interference arising from cardiac activity, respiration, and other homeostatic processes (Obrig et al., 2000
; Tonorov et al., 2000
; Payne et al., 2009
; Diamond et al., 2009
). These sources of interference are called global interference or systemic interference. Part of the interference occurs both in the superficial layers of the head (scalp and skull) and in the brain tissue itself. However, the back-reflection geometry of the measurement makes NIRS significantly more sensitive to the superficial layers. As such, the NIRS signal is often dominated by systemic interference occurring in the skin and the skull.
Different methods have been used in the literature to remove the systemic interference from DOI measurements. Low pass filtering is widely used in the literature, as it is highly effective at removing cardiac oscillations (Franceschini et al., 2003
; Jasdzewski et al., 2003
). However, there is a significant overlap between the frequency spectrum of the hemodynamic response to brain activity and the spectrum of other physiological variations such as respiration, spontaneous low frequency oscillations and very low frequency oscillations. Frequency-based removal of these sources of interference can therefore result in large distortion and inaccurate timing for the recovered brain activity signal. As such, more powerful methods for global noise reduction have been developed. These include adaptive average waveform subtraction (Gratton and Corballis, 1995
), subtraction of another NIRS source-detector (SD) channel performed over a non-activated region of the brain (Franceschini et al., 2003
), principal component analysis (Zhang et al., 2005
; Franceschini et al., 2006
) and finally wavelet filtering (Lina et al., 2008
; Matteau-Pelletier et al., 2009
; Jang et al., 2009
; Lina et al., 2010
A recent development for removing global interference from NIRS measurements is to use additional optodes in the activated region with small SD separations that are sensitive to superficial layers only (Saager and Berger, 2008
; Zhang et al., 2007a
; Umeyama and Yamada, 2009
; Yamada et al., 2009
; Gregg et al., 2010
). Making the assumption that the signal collected in the superficial layers is dominated by systemic physiology which is also dominant in the longer SD separation NIRS channel, those additional measurements can be used as regressors to filter systemic interference from the longer SD separations. Saager et al (2005)
used additional optodes and a linear minimum mean square estimator (LMMSE) to partially remove the systemic interference in the signal. In a second step, the evoked hemodynamic response was estimated using a traditional block-average method over the different trials. The algorithm was further refined by Zhang et al (2007a
) to consider the non-stationary behavior of the systemic interference. They used an adaptive filtering technique together with additional small separation measurements to filter the systemic interference from the raw signal and then performed the block-average technique to estimate the hemodynamic response in a second step.
Although these methods greatly reduced global interference in NIRS data, the filtering of the systemic interference and the estimation of the hemodynamic response were performed in two steps, which might not be optimal. Previous studies have shown that the simultaneous estimation of the hemo-dynamic response and removal of the systemic interference using temporal basis functions (Kolehmainen et al., 2003
; Prince et al., 2003
) or auxiliary systemic measurements (Diamond et al., 2006
) was possible using state-space modeling. Moreover, Diamond et al proposed a way to quantify the accuracy of such filtering methods. Real NIRS data collected over the head of human subjects at rest were used to generate realistic noise. A synthetic hemodynamic response was added over the real NIRS baseline time course and the response was then recovered from this noisy data set. The recovered response was then compared with the synthetic one used to generate the time course. This method for evaluating reconstruction algorithms has been reproduced by other groups (Lina et al., 2008
; Matteau-Pelletier et al., 2009
; Lina et al., 2010
In the present study, we combined small separation measurements and state-space modeling for the estimation of the hemodynamic response and simultaneous global interference cancellation. We developed both a static and a dynamic estimator. We evaluated the performance of our algorithms using baseline data taken from 6 human subjects at rest and by adding a synthetic hemodynamic response over the baseline measurements. We finally compared our new methods with the adaptive filter (Zhang et al., 2007a
) and the standard method using no small SD separation measurement.