A magnetic resonance imaging (MRI) compatible near infrared spectrometer (NIRS) system was developed and evaluated for continuous-wave diffuse optical spectroscopy (DOS) and concurrent functional MRI measurements of human muscle. Phantom and in-vivo experiments using the system’s fiber bundle suggested that an isolation distance greater than 8 mm needs to exist between adjacent illumination-detection channels. Using single and probe-pair arrangements (inter-fiber separations of 80 µm and 5 mm, respectively), in-vivo DOS point-measurements (total=20 images) were performed on 1) the antecubital vein and a reference tissue area and 2) the lower leg at the medial (MG) and lateral gastrocnemius (LG) under isokinetic exercise. Mean spectral morphological differences and relative mean intensity changes at Hemoglobin key wavelengths were found, namely reduced mean pixel intensity (~30%) for the vessel-area and a signal change of ~1-4% between the rest and the recovery condition at both muscle locations for the single-probe configuration. Subsequent work is necessary to evaluate the oxygenation assessment capabilities of this system. Lastly, experiments were performed in which two volunteers had concurrent measurement of optical and blood oxygen level dependent (BOLD) MRI, before and following exercise. The same probe arrangement was used for DOS measurements for this experiment. The BOLD signal was studied for manually-derived ROIs. BOLD recovery curves corresponding to the LG followed routine temporal progression where immediate post-exercise signal is hypointense, followed with a sigmoidal-shaped recovery. A decrease ranging between ~0.1-20% was found in the normalized mean spectral signal (20 images) for recovery with reference to the rest condition at both muscle locations for single-probe measurements and for one probe-pair measurement (for 800,808 and 850 nm). The specific trend of the measured decrease in the mean spectral curves during recovery was not consistent among these trials. Future steps include repeatable phantom experiments, increased optical power delivery, enhanced skin contact and improved reflectance measurements / Thesis / Master of Applied Science (MASc)

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Introduction: Currently, there are no objective techniques to assess burn depth. An early assessment of burn depth would enable accurate management decisions, which would improve patient outcomes. Near infrared (NIR) technology has shown promise as a non-invasive monitor of oxygenation and perfusion, and its potential to assess the depth of burn injuries has been investigated clinically over the past five years. The purpose of the thesis was to determine the capacity of NIR technology to differentiate acute thermal injuries. Methods: Burn sites (n=5) and control sites (n=5) were created on the dorsum of sixteen animals with brass rods held at constant pressure and heated to 100°C and 37.5°C respectively. NIR data was collected from the burns and control sites pre-burn, immediately post-burn, and 1, 12, 24, 36, 48 and 96 hours after the burn injury. Biopsies of the burn and control sites were acquired at each time point and used to confirm the depth of injury. NIR data was processed for the content of water, oxy-, deoxy- and methemoglobin. Results: Oxyhemoglobin and total hemoglobin decreased as burn depth increased. The proportion of oxy- and deoxyhemoglobin to total hemoglobin showed that the ratio of oxy- to deoxyhemoglobin decreased as burn injury increased. Methemoglobin levels as a ratio of total hemoglobin also showed that as the severity of injury increased the proportion of methemoglobin also increased. Finally, superficial partial thickness injuries (3 s and 12 s) showed early peak levels of water, which rapidly declined towards baseline. The deep partial thickness injuries (20 s and 30 s) do not experience peak levels and retain water over the course of the experiment. The full thickness injuries water levels remain close or below baseline levels throughout the experiment. Conclusion: NIR spectroscopy could distinguish burn depth using water, oxy-, met- and total hemoglobin as separate entities. The presence of methemoglobin in the burn wounds is a novel finding that has not been described previously in burn literature.

Diabetes Mellitus is a major health problem which affects about 200 million people worldwide. Diabetics require their blood glucose levels to be kept within the normal range in order to prevent diabetes-related complications from occurring. Blood glucose measurement is therefore of vital importance. The current glucose measurement techniques are, however, painful, inconvenient and episodic. This document provides an investigation into the use of near-infrared spectroscopy for continuous, non-invasive measurement of blood glucose. Artificial neural networks are used for the development of multivariate calibration models which predict glucose concentrations based on the near-infrared spectral data. Simulations have been performed which make use of simulated spectral data generated from the characteristic spectra of many of the major components of human blood. The simulations show that artificial neural networks are capable of predicting the glucose concentrations of complex aqueous solutions with clinically relevant accuracy. The effect of interference, such as temperature changes, pathlength variations, measurement noise and absorption due other analytes, has been investigated and modelled. The artificial neural network calibration models are capable of providing acceptably accurate predictions in the presence of multiple forms of interference. It was found that the performance of the measurement technique can be improved through careful selection of the optical pathlength and wavelength range for the spectroscopic measurements, and by using preprocessing techniques to reduce the effect of interference. Although the simulations suggest that near-infrared spectroscopy is a promising method of blood glucose measurement, which could greatly improve the quality of life of diabetics, many further issues must be resolved before the long-term goal of developing a continuous non-invasive home glucose monitor can be achieved.

Yeung Shun-hin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 52-54). / Abstracts in English and Chinese. / TITLE PAGE --- p.i / THESIS COMMITTEE --- p.ii / ABSTRACT (ENGLISH) --- p.iii / ABSTRACT (CHINESE) --- p.iv / ACKNOWLEDGEMENTS --- p.v / TABLES OF CONTENTS --- p.vi / LIST OF FIGURES --- p.viii / LIST OF TABLES --- p.x / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Computer-controlled Data Acquisition and Frequency Calibration System for a Ti: sapphire laser spectrometer --- p.3 / Chapter Section 2A --- Motivation and Overview --- p.3 / Chapter Section 2B --- The Hardware --- p.5 / Chapter Section 2C --- The Program --- p.12 / Chapter Section 2D --- Summary --- p.27 / Chapter Chapter 3 --- Cavity Enhanced Absorption Spectroscopy Using Phase-Sensitive Detection --- p.28 / Chapter Section 3A --- Motivation --- p.28 / Chapter Section 3B --- Cavity ring-down technique: the background --- p.29 / Chapter Section 3C --- Cavity enhanced absorption spectroscopy: a historical review --- p.34 / Chapter Section 3D --- Experimental Apparatus --- p.37 / Chapter Section 3E --- Results of Performance tests --- p.41 / Chapter Section 3F --- Applications --- p.45 / Chapter Section 3G --- Summary --- p.49 / Chapter Chapter 4 --- Concluding Remarks --- p.50 / REFERENCES --- p.52

Noninvasive glucose sensing has been studied widely. Near infrared (NIR) absorption spectroscopy and Raman scattering spectroscopy are proposed individually and combined as methods for glucose measurement in a three component sample matrix. In both techniques, the light transmits through human skin and a spectrum is collected. The research described in this thesis is like this. The use of individual NIR spectra data and individual Raman spectra data can give a good prediction ability of the partial least-squares (PLS) calibration model. Since the NIR and Raman spectroscopies have complementary nature of molecular vibrations, the research tried to prove the prediction ability of the PLS calibration model can be improved by combining NIR and Raman spectra data. Two approaches are investigated to ascertain the benefits of combining these spectral methods. First, NIR and Raman spectral data collected from a set of 60 samples concated and used to compute multivariate models based on PLS and net analyte signal (NAS) methods. The performance of models based on concated NIR-Raman spectra are compared to conventional models based on only NIR and only Raman spectra. The second strategy reported in this chapter is the simulated NIR and Raman spectra and computing PLS and NAS models by concating these simulated spectra. Spectral simulation permits systematic variations in noise levels. In both cases, various preprocessing methods are explored to find a suitable way to combine the different spectral types. The result from the real spectra data is that adding low signal-to-noise ratio (SNR) to high SNR spectra would make the calibration models worse. The result from the simulated spectra data is that with the same SNR and the same magnitude of the two spectra, the prediction ability of the calibration model can be improved.

Brain-computer interface (BCI) technologies allow users to control external devices through brain activity alone, circumventing the somatic nervous system and the need for overt physical movement. BCIs may potentially benefit individuals with severe neuromuscular disorders who experience significant, and often total, loss of voluntary muscle control (e.g. amyotrophic lateral sclerosis, multiple sclerosis, brainstem stroke). Though a majority of BCI research to date has focused on electroencephalography (EEG) for brain signal acquisition, recently researchers have noted the potential of an optical imaging technology called near-infrared spectroscopy (NIRS) for BCI applications. This thesis investigates the feasibility of a practical, online optical BCI based on conscious modulation of prefrontal cortex activity through the performance of different cognitive tasks, specifically mental arithmetic (MA) and mental singing (MS). The thesis comprises five studies, each representing a step toward the realization of a practical optical BCI. The first study demonstrates the feasibility of a two-choice synchronized optical BCI based on intentional control states corresponding to MA and MS. The second study explores a more user-friendly alternative - a two-choice system-paced BCI supporting a single intentional control state (either MA or MS) and a natural baseline, or "no-control (NC)", state. The third study investigates the feasibility of a three-choice system-paced BCI supporting both MA and MS, as well as the NC state. The fourth study examines the consistency with which the relevant mental states can be differentiated over multiple sessions. The first four studies involve healthy adult participants; in the final study, the feasibility of optical BCI use by a user with Duchenne muscular dystrophy is explored. In the first study, MA and MS were classified with an average accuracy of 77.2% (n=10), while in the second, MA and MS were differentiated individually from the NC state with average accuracies of 71.2% and 62.7%, respectively (n=7). In the third study, an average accuracy of 62.5% was obtained for the MA vs. MS vs. NC problem (n=4). The fourth study demonstrated that the ability to classify mental states (specifically MA vs. NC) remains consistent across multiple sessions (p=0.67), but that there is intersession variability in the spatiotemporal characteristics that best discriminate the states. In the final study, a two-session average accuracy of 71.1% was achieved in the MA vs. NC classification problem for the participant with Duchenne muscular dystrophy.

Individuals with limited motor control are often unable to interact with their environment. Recently, near-infrared spectroscopy (NIRS) systems have been investigated as potential brain-computer interfaces (BCI). Previous studies examined data offline, preventing users from understanding how their thoughts triggered the NIRS system. This thesis focused on understanding the short-term effects of feedback on user’s ability to learn how to control BCIs. Data were collected from control and experimental groups over seven sessions, as they performed fast singing imagery or mental arithmetic. Significant differences were observed between the control group’s results in non-feedback sessions and the experimental group’s results in feedback sessions. Qualitative results from 3 of the 10 participants suggested they had control of the feedback system. They performed the task with online accuracies of 61% - 88% in the final 2 sessions with feedback. These results suggest that continued investigation of NIRS feedback systems is warranted.

Near-infrared spectroscopy (NIRS) is an emerging non-invasive brain-computer interface (BCI) modality that measures changes in hemoglobin concentrations in neurocortical tissue. Previous NIRS studies have not employed real-time feedback with online classification, a combination which would allow users to alter their mental strategy on the fly. This thesis reports the results of two online studies. The first study contrasted online classification of prefrontal hemodynamics using an artificial neural network (ANN) and a hidden Markov model-based (HMM) classifier. The second study measured the accuracy of an online linear discriminant classifier. In study 1, only the ANN classifier facilitated online classification rates greater than chance (p=0.0289). In study 2, a new feedback system and experimental protocol led to improved classification rates over those of the first study (p=5.1*10^(-5)). While control over instantaneously generated feedback in online NIRS-BCIs has been demonstrated, factors such as user frustration, mental fatigue, and restrictions on ambient lighting may compromise performance.

Near-infrared spectroscopy (NIRS) is an emerging non-invasive brain-computer interface (BCI) modality that measures changes in hemoglobin concentrations in neurocortical tissue. Previous NIRS studies have not employed real-time feedback with online classification, a combination which would allow users to alter their mental strategy on the fly. This thesis reports the results of two online studies. The first study contrasted online classification of prefrontal hemodynamics using an artificial neural network (ANN) and a hidden Markov model-based (HMM) classifier. The second study measured the accuracy of an online linear discriminant classifier. In study 1, only the ANN classifier facilitated online classification rates greater than chance (p=0.0289). In study 2, a new feedback system and experimental protocol led to improved classification rates over those of the first study (p=5.1*10^(-5)). While control over instantaneously generated feedback in online NIRS-BCIs has been demonstrated, factors such as user frustration, mental fatigue, and restrictions on ambient lighting may compromise performance.