Study Of Signal Extraction Method In Brain Activity Measurement By Near Infrared Spectroscopy

Near infrared spectroscopy (NIRS) allows the non-invasive measurement of haemody-namic variables and is particularly suited to the detection of changes in concentrationsof oxy- and deoxy-haemoglobin in the brain, thereby providing insights into metabol-ic events in the cerebral cortex. Consequently, NIRS has been developed to measurebrain activity, leading to what has become the well-recognised method of functional near-infrared spectroscopy (fNIRS). fNIRS may be compared with other techniques, such aselectroencephalography (EEG), magnetoencephalography (MEG), positron emission to-mography (PET), and functional magnetic resonance imaging (fMRI). It does appear tohave several advantages over these other methods, such as portability, fewer physicalrestrictions and greater practicality, good temporal resolution, safety, and inexpensiveinstrumentation, and thus has a very broad application prospect. However, there are prob-lems in using fNIRS due to the presence of physiological interference.The physiological interference when using fNIRS arises mainly from perturbationscaused by cardiac events, breathing, low frequency oscillations (LFOs), and very lowfrequency oscillations (VLFOs). All of these interference sources are located both in thevasculature of the superficial layer of the brain and deeper inside the brain. This has meantthat without appropriate interference reduction the full potential of fNIRS has not yet beenrealised. Therefore, this thesis studies several practical methodologies to overcome thephysiological interference problem in fNIRS, aiming to improve the detection accuracyof brain activity measurement and promote the further development and utilization of themethod.The main contents of this dissertation are as follows:(1) A justification for the use of Monte Carlo simulation is given. A truly rigorousevaluation of fNIRS in vivo requires an uncontaminated evoked brain activity responsesignal as a standard, which, unfortunately, is unavailable. In addition, the partial volumeeffect (PVE) cannot be precisely compensated for in vivo and the quantitative comparisonof the recovery response and the true response of brain activity is therefore difficult. Thus,the Monte Carlo method, based on a five-layered adult head model, was implemented tosimulate the brain activity process for optical measurement. By compensating for the PVE, a simulation software package was developed to serve as the evaluation tool fordifferent methods for the extraction of brain activity measurements.(2) The use of Empirical Mode Decomposition (EMD) for brain signal extraction isdescribed. The single-distance NIRS probe configuration is often used to measure thehaemodynamic changes, both for monitoring and for imaging based on grids of sourcesand detectors, because it has the advantages of the simplicity of the optical probe andgreater practicality. Low pass filtering techniques have been used in attempts to suppressphysiological interference and these have been moderately successful for removal of theinterference caused by cardiac oscillations. However, low pass filtering may not be appro-priate for other specific physiological noise, such as that produced by breathing since suchnoise is difficult to be distinguished from the genuine haemodynamic response to brainactivity by frequency characteristics alone and thus it is not possible to design the low passfilter with a fixed cut-off frequency. Therefore, a methodology based on EMD is proposedto extract the signal of brain activity for single-distance measurement. The accuracy ofthe brain activity measurement is improved by utilizing EMD because it can be used toremove interference arising from the cardiac events and breathing. The effectiveness ofthis methodology has been proved by means of the software package.(3) The application of Recursive Least Squares (RLS) adaptive filtering is described.Compared with cardiac and respiratory interference, the suppression of LFOs and VL-FOs is relatively difficult with ordinary filtering techniques because these frequencies andthose of the functional activity may severely overlay each other. In fNIRS measurementvery useful information may be in the deep tissue (gray matter) and light inevitably inter-acts with blood in layers other than gray matter. Considering that the penetration depth ofNIRS is related to the source-detector separation, a methodology of combining a multi-distance probe and recursive least square (RLS) adaptive filtering is proposed. We madethe measurement acquired from NIRS short source-detector separation as the referencesignal and the measurement acquired from NIRS long source-detector separation as thedesired signal. The least mean square (LMS) and RLS algorithms are implemented tocompare the accuracy and the convergence rate. We derived measurements by adoptingdifferent interoptode distances, which is relevant to the process of optimizing the NIRSprobe configuration. The in?uence of superficial layer thickness on the performance of theRLS algorithm was also investigated. The simulation results demonstrated that the RLS algorithm has a faster convergence and smaller mean squared error (MSE) than the LMSalgorithm. The MSE for different probe configuration and superficial layer thickness arealso calculated based on statistical methods.(4) The combination of EMD and RLS was explored. Physiological interference can beinduced by different physiological phenomena and thus it contains multiple components.When the brain exhibits some haemodynamic heterogeneity, the different interferencecomponents may produce dissimilarities between the superficial layers and the cortex, orin different locations. In our study presented here we adopt the multidistance measure-ment method and a theoretical analysis of global interference reduction based on EMDand the least squares criterion. The short-distance fNIRS measurement is treated as com-prising of superficial haemodynamic changes induced by physiological ?uctuations andthe long-distance fNIRS measurement is the functional haemodynamic response contam-inated by global interference. By decomposing superficial haemodynamic ?uctuationswith the EMD algorithm, we separated the interference into different intrinsic mode func-tions (IMFs) possessing distinct frequency characteristics. The recursive least squaresmethod was then used to adjust the corresponding weighting coefficients to estimate glob-al interference with the obtained IMFs. The experimental results demonstrate that optimalalgorithms have higher precision that RLS adaptive filter when the brain tissue presentssome degree of heterogeneity.(5) In vivo measurements with multi-distance NIRS were investigated. To further studythe brain activity with fNIRS and evaluate the effectiveness of the proposed method, aNIRS system was developed based on a multi-distance measurement configuration andcontinuous wave spectroscopy. The performance of the system was verified by the in vit-ro model experiment and in vivo forearm occlusion experiments. Subsequently a block-design experiment was conducted on auditory stimuli and the evoked response of thecortex in the temporal region was continuously monitored and further analyzed by theRLS algorithm. The experimental results show that the temporal area is sensitive to mu-sic stimuli. The comparison of the original results and the RLS results demonstrate thefeasibility and effectiveness of RLS adaptive filtering for fNIRS.