Modified Beer-Lambert Law Research Articles

Origin of the anisotropic Beer–Lambert law from dichroism and birefringence in β -Ga2O3

The anisotropic optical absorption edge of β-Ga2O3 follows a modified Beer–Lambert law having two effective absorption coefficients. The absorption coefficient of linearly polarized light reduces to the least absorbing direction beyond a critical penetration depth, which itself depends on polarization and wavelength. To understand this behavior, a Stokes vector analysis is performed to track the polarization state as a function of depth. The weakening of the absorption coefficient is associated with a gradual shift of linear polarization to the least absorbing crystallographic direction in the plane, which is along the a-exciton within the (010) plane or along the b-exciton in the (001) plane. We show that strong linear dichroism near the optical absorption edge causes this shift in β-Ga2O3, which arises from the anisotropy and spectral splitting of the physical absorbers, i.e., excitons. The linear polarization shift is accompanied by a variation in the ellipticity due to the birefringence of β-Ga2O3. Analysis of the phase relationship between the incoming electric field to that at a certain depth reveals the phase speed as an effective refractive index, which varies along different crystallographic directions. The critical penetration depth is shown to be correlated with the depth at which the ellipticity is maximal. Thus, the anisotropic Beer–Lambert law arises from the interplay of both the dichroic and birefringent properties of β-Ga2O3.

On the Origin of the Photoplethysmography Signal: Modeling of Volumetric and Aggregation Effects

This study aimed to examine the mechanisms of the photoplethysmography (PPG) signal formation using Monte Carlo simulations of light transport in biological tissues and experimental observations. Based on a three-layer skin model in backscattering geometry, we sequentially simulated volumetric blood changes and the aggregation/disaggregation of erythrocytes in the dermal layer and estimated their contribution to the registered PPG signal. The calculations were conducted for two wavelengths: 525 nm and 810 nm. For green light, absorption predominates over scattering in the formation of a PPG signal, whereas, for near-infrared light, scattering prevails over absorption. This theoretical result was verified using the Modified Beer–Lambert law and clinical in vivo PPG data of seven healthy subjects. Changes in the size of the scatterers during erythrocyte aggregation and disaggregation can significantly contribute to the PPG signal at near-infrared light. Thus, for the green waveband, the classical volumetric model can be considered dominant in the PPG signal formation. In contrast, for the near-infrared range, both volumetric and aggregation effects must be considered as being approximately equal.

Investigating Skin Cancer Diagnosis Using a Webcam-based Microcontroller System

Skin cancer can spread fast to nearby tissue and other parts of the human body if it's not diagnosed early. Most are curable only if skin cancer is found and treated in the early stages. Therefore, it's essential to seek a casual way of early diagnosis. This paper assesses a prototype system for skin cancer detection using an Arduino with an ArduCam Mega 5MP, benchmarked against smartphone diagnosis. Bandpass filters capture skin images at red (650 nm), green (532 nm), and blue (450 nm) wavelengths, measuring reflectance values. The approach aims to quantitatively determine skin melanin, oxyhemoglobin, and deoxyhemoglobin levels, aiding in various skin lesions' diagnosis. Evaluation involves comparing pixel reflectance values of images taken by smartphones and the prototype using a 3D mesh grid. Applying the modified Lambert-Beer law to reflectance values of moles, pimples, scars, scabs, and traces predicts relative levels of skin components. The system shows an 87% match with the smartphone standard, demonstrating high reliability. Further study might be needed to clarify the confirmation with clinical cases.

Cerebral oxygenation and perfusion kinetics monitoring of military aircrew at high G using novel fNIRS wearable system.

Real-time physiological episode (PE) detection and management in aircrew operating high-performance aircraft (HPA) is crucial for the US Military. This paper addresses the unique challenges posed by high acceleration (G-force) in HPA aircrew and explores the potential of a novel wearable functional near-infrared spectroscopy (fNIRS) system, named NIRSense Aerie, to continuously monitor cerebral oxygenation during high G-force exposure. The NIRSense Aerie system is a flight-optimized, wearable fNIRS device designed to monitor tissue oxygenation 13-20 mm below the skin's surface. The system includes an optical frontend adhered to the forehead, an electronics module behind the earcup of aircrew helmets, and a custom adhesive for secure attachment. The fNIRS optical layout incorporates near-distance, middle-distance, and far-distance infrared emitters, a photodetector, and an accelerometer for motion measurements. Data processing involves the modified Beer-Lambert law for computing relative chromophore concentration changes. A human evaluation of the NIRSense Aerie was conducted on six subjects exposed to G-forces up to +9 Gz in an Aerospace Environmental Protection Laboratory centrifuge. fNIRS data, pulse oximetry, and electrocardiography (HR) were collected to analyze cerebral and superficial tissue oxygenation kinetics during G-loading and recovery. The NIRSense Aerie successfully captured cerebral deoxygenation responses during high G-force exposure, demonstrating its potential for continuous monitoring in challenging operational environments. Pulse oximetry was compromised during G-loading, emphasizing the system's advantage in uninterrupted cerebrovascular monitoring. Significant changes in oxygenation metrics were observed across G-loading levels, with distinct responses in Deoxy-Hb and Oxy-Hb concentrations. HR increased during G-loading, reflecting physiological stress and the anti-G straining maneuver. The NIRSense Aerie shows promise for real-time monitoring of aircrew physiological responses during high G-force exposure. Despite challenges, the system provides valuable insights into cerebral oxygenation kinetics. Future developments aim for miniaturization and optimization for enhanced aircrew comfort and wearability. This technology has potential for improving anti-G straining maneuver learning and retention through real-time cerebral oxygenation feedback during centrifuge training.

Artificial neural network models: implementation of functional near-infrared spectroscopy-based spontaneous lie detection in an interactive scenario.

Deception is an inevitable occurrence in daily life. Various methods have been used to understand the mechanisms underlying brain deception. Moreover, numerous efforts have been undertaken to detect deception and truth-telling. Functional near-infrared spectroscopy (fNIRS) has great potential for neurological applications compared with other state-of-the-art methods. Therefore, an fNIRS-based spontaneous lie detection model was used in the present study. We interviewed 10 healthy subjects to identify deception using the fNIRS system. A card game frequently referred to as a bluff or cheat was introduced. This game was selected because its rules are ideal for testing our hypotheses. The optical probe of the fNIRS was placed on the subject's forehead, and we acquired optical density signals, which were then converted into oxy-hemoglobin and deoxy-hemoglobin signals using the Modified Beer-Lambert law. The oxy-hemoglobin signal was preprocessed to eliminate noise. In this study, we proposed three artificial neural networks inspired by deep learning models, including AlexNet, ResNet, and GoogleNet, to classify deception and truth-telling. The proposed models achieved accuracies of 88.5%, 88.0%, and 90.0%, respectively. These proposed models were compared with other classification models, including k-nearest neighbor, linear support vector machines (SVM), quadratic SVM, cubic SVM, simple decision trees, and complex decision trees. These comparisons showed that the proposed models performed better than the other state-of-the-art methods.

A priori free spectral unmixing with periodic absorbance changes: application for auto-calibrated intraoperative functional brain mapping.

Spectral unmixing designates techniques that allow to decompose measured spectra into linear or non-linear combination of spectra of all targets (endmembers). This technique was initially developed for satellite applications, but it is now also widely used in biomedical applications. However, several drawbacks limit the use of these techniques with standard optical devices like RGB cameras. The devices need to be calibrated and a a priori on the observed scene is often necessary. We propose a new method for estimating endmembers and their proportion automatically and without calibration of the acquisition device based on near separable non-negative matrix factorization. This method estimates the endmembers on spectra of absorbance changes presenting periodic events. This is very common in in vivo biomedical and medical optical imaging where hemodynamics dominate the absorbance fluctuations. We applied the method for identifying functional brain areas during neurosurgery using four different RGB cameras (an industrial camera, a smartphone and two surgical microscopes). Results obtained with the auto-calibration method were consistent with the intraoperative gold standards. Endmembers estimated with the auto-calibration method were similar to the calibrated endmembers used in the modified Beer-Lambert law. The similarity was particularly strong when both cardiac and respiratory periodic events were considered. This work can allow a widespread use of spectral imaging in the industrial or medical field.

Self-calibrated pulse oximetry algorithm based on photon pathlength change and the application in human freedivers.

Pulse oximetry estimates the arterial oxygen saturation of hemoglobin () based on relative changes in light intensity at the cardiac frequency. Commercial pulse oximeters require empirical calibration on healthy volunteers, resulting in limited accuracy at low oxygen levels. An accurate, self-calibrated method for estimating is needed to improve patient monitoring and diagnosis. Given the challenges of calibration at low levels, we pursued the creation of a self-calibrated algorithm that can effectively estimate across its full range. Our primary objective was to design and validate our calibration-free method using data collected from human subjects. We developed an algorithm based on diffuse optical spectroscopy measurements of cardiac pulses and the modified Beer-Lambert law (mBLL). Recognizing that the photon mean pathlength () varies with related absorption changes, our algorithm aligns/fits the normalized (across wavelengths) obtained from optical measurements with its analytical representation. We tested the algorithm with human freedivers performing breath-hold dives. A continuous-wave near-infrared spectroscopy probe was attached to their foreheads, and an arterial cannula was inserted in the radial artery to collect arterial blood samples at different stages of the dive. These samples provided ground-truth via a blood gas analyzer, enabling us to evaluate the accuracy of estimation derived from the NIRS measurement using our self-calibrated algorithm. The self-calibrated algorithm significantly outperformed the conventional method (mBLL with a constant ratio) for estimation through the diving period. Analyzing 23 ground-truth data points ranging from 41% to 100%, the average absolute difference between the estimated and the ground truth is for our algorithm, significantly lower than the observed with the conventional approach. By factoring in the variations in the spectral shape of relative to , our self-calibrated algorithm enables accurate estimation, even in subjects with low levels.

Differences of cerebral oxygen saturation in dialysis patients: a comparison of three principals of near infrared spectroscopy.

It has been reported that cerebral oxygen saturation (rSO2) measured by near infrared spectroscopy is low in dialysis patients. We compared the rSO2 values of dialysis patients before living donor kidney transplantation and their donors as controls by using three spectroscopes that utilize different principals, the INVOS 5100C (spatially resolved spectroscopy), FORE-SIGHT ELITE (modified Beer-Lambert law) and tNIRS-1 (time-resolved spectroscopy). Before induction of anesthesia, the sensors of one of the three spectroscopes were placed on the forehead and rSO2 values were recorded followed by the same measurement using the other two spectroscopes. The primary objective was to compare the rSO2 values of the dialysis patients and controls using the three spectroscopes by the unpaired t test. Then we compared the rSO2 values among the spectroscopes in both dialysis patients and controls by one-way ANOVA. Finally, we examined the relations between the rSO2 values and the physiological values by using the Pearson correlation coefficient. Fifteen pairs of dialysis patients and controls were studied. With the INVOS 5100C, the values of the dialysis patients (59.7 ± 9.7% (mean ± standard deviation) were 13% lower than those of the controls (73.3 ± 6.9%) (P < 0.01). With the tNIRS-1, the values were 57.8 ± 4.8% in the dialysis patients and 63.3 ± 3.5% in the controls (P < 0.01). Almost no differences were observed with the FORE-SIGHT ELITE (71.6 ± 4.9% [dialysis patients] vs. 70.8 ± 4.3% [Controls]) (P = 0.62). Among the spectroscopes, the values were significantly different in both dialysis patients and controls. For the INVOS 5100C and tNIRS-1, correlation coefficients between rSO2 values and blood Hb and serum Alb were more than 0.5. The rSO2 values for comparisons between the dialysis patients and the controls were different according to differences of the principles of the near infrared spectroscopes. In the INVOS 5100C and tNIRS-1, rSO2 values may be related to blood Hb and serum Alb.

ECG augmented pulse oximetry in Atlantic salmon (Salmo salar)—A pilot study

Understanding the tolerance limits of fish is crucial for developing aquaculture operations that ensure good animal welfare. However, there exist little data describing the physiological responses in farmed Atlantic salmon, much because the technological tools for taking such measurements have not existed. Recent advances in electronic implants have enabled concurrent measurement of electrocardiogram (ECG) and photoplethysmograms (PPG) in salmon that can be used for robust estimation of HR and oxygen saturation in arterial blood (i.e., SpO2/pulse oximetry) if appropriate strategies for motion artifact and light scattering compensation can be realized. To enable pulse oximetry for farmed Atlantic salmon (and fish in general), two experiments have been conducted. In Experiment 1, PPGs were obtained from salmon induced to swim at two different water currents under normoxic conditions. By using two water currents, the resulting data provided a foundation for developing methods for motion artifact compensation. Data from this experiment were also used to calculate an average light scattering parameter using the modified Beer–Lambert law, under the assumption that SpO2 was 100% for individual fish. In Experiment 2, fish were placed in a swim tunnel and subjected to hypoxic conditions and corresponding changes in SpO2 were estimated using the motion artifact and light scattering compensation approaches from Experiment 1.Results show that the suggested compensation approaches gives SpO2 estimates within the expected range (95% to 100%) under normoxic conditions. Under hypoxic conditions, changes in SpO2 that coincide with experiment events were observed, demonstrating that PPGs can be used to quantify such changes. The results from this pilot study therefore extend the selection of physiological parameters feasible to measure using electronic implants for Atlantic salmon. In doing so, the scope for physiological measurements is extended such that an improved understanding of physiological responses and tolerances in Atlantic salmon farming can be acquired, and ultimately be used to improve animal welfare in fish production.

Development of a Tissue Oxygenation Flow-Based Index Toward Discerning the Healing Status in Diabetic Foot Ulcers.

Objective: The objective of this study is to characterize breath-hold (BH)-induced oxygenation changes in diabetic foot ulcers (DFUs) and develop an oxygenation flow index (OFI) to discern nonhealing from healing DFUs. Approach: The imaging approach utilizes an innovative BH stimulus that induces vasoconstriction and measures for altering oxygenation flow in and around the tissues of DFUs and controls. The modified Beer-Lambert law was utilized to calculate hemoglobin-based spatiotemporal oxygenation maps in terms of oxygen saturation. Results: We found controls had synchronous BH-induced oxygenation changes across the dorsal (OFI: 29.0%) and plantar (OFI: 57.6%) aspects of the foot. Nonhealing DFUs, however, had less synchronous BH-induced oxygenation changes (OFI <28%). In addition, two complicated healing DFU cases, or cases with underlying issues or poor long-term healing outcomes, were observed to have OFIs <28%. Innovation: An OFI was developed to differentiate nonhealing DFUs from healing DFUs using a single, noncontact, near-infrared optical scanner for spatiotemporal oxygenation monitoring. The OFI has potential to provide immediate feedback on the microcirculation in DFUs, through hemoglobin-based oxygenation parameters. Conclusion: A preliminary threshold (OFI <28%) could differentiate nonhealing and complicated DFUs from healing DFUs. The overall oxygenation flow pattern was less synchronous (or the OFI value reduced) in the nonwound areas of the feet that were nonhealing. In other words, the reduced OFI value (<28%) in the entire foot, excluding the wound region is a possible indicator that the wound may not heal.

One-dimensional physical model for complementary electrochromic device

Eletrochromic devices are electrochemical systems that can undergo the optical modulation in response to an applied electrical stimulus. In order to investigate the electrochromic (EC) process mechanism and predict the electrochromic behavior, this paper proposes a physical model that employs tungsten trioxide (WO3), nickel oxide (NiO) and LiClO4-propylene carbonate (PC) solution. Within electrochromic films, electrolytes can transport lithium ions and anions through porous layers of electrochromic films. At the interfaces between solution and porous layers, lithium-ion intercalation and deintercalation take place. Considering both ion diffusion and electromigration, ion transport kinetics is described by Nernst-Plank equation. The partial differential equations (PDEs) for potential consist of Poisson equations for electrolytes and Ohm’s law for WO3 and NiO films. Moreover, the ion injection behavior at the interface is governed by Frumkin-Butler-Volmer (FBV) equation and potential conditions of the stern layer. Finally, a modified Beer–Lambert law incorporating porosity is proposed to explain the mechanism of transmittance. Under constant step potential conditions, the state variables of multiphysics field can be tracked, and the dynamic process of the transmittance and electrode current can be accurately predicted. This physical model can be applied for parameter design and precise control of ECDs, based on the optimization of device characteristics.

Single-leg stance on a challenging surface can enhance cortical activation in the right hemisphere – A case study

Maintaining body balance, whether static or dynamic, is critical in performing everyday activities and developing and optimizing basic motor skills. This study investigates how a professional alpine skier's brain activates on the contralateral side during a single-leg stance. Continuous-wave functional near-infrared spectroscopy (fNIRS) signals were recorded with sixteen sources and detectors over the motor cortex to investigate brain hemodynamics. Three different tasks were performed: barefooted walk (BFW), right-leg stance (RLS), and left-leg stance (LLS). The signal processing pipeline includes channel rejection, the conversation of raw intensities into hemoglobin concentration changes using modified Beer-Lambert law, baseline zero-adjustments, z-normalization, and temporal filtration. The hemodynamic brain signal was estimated using a general linear model with a 2-gamma function. Measured activations (t-values) with p-value <0.05 were only considered as statistically significant active channels. Compared to all other conditions, BFW has the lowest brain activation. LLS is associated with more contralateral brain activation than RLS. During LLS, higher brain activation was observed across all brain regions. The right hemisphere has comparatively more activated regions-of-interest. Higher ΔHbO demands in the dorsolateral prefrontal, pre-motor, supplementary motor cortex, and primary motor cortex were observed in the right hemisphere relative to the left which explains higher energy demands for balancing during LLS. Broca's temporal lobe was also activated during both LLS and RLS. Comparing the results with BFW– which is considered the most realistic walking condition–, it is concluded that higher demands of ΔHbO predict higher motor control demands for balancing. The participant struggled with balance during the LLS, showing higher ΔHbO in both hemispheres compared to two other conditions, which indicates the higher requirement for motor control to maintain balance. A post-physiotherapy exercise program is expected to improve balance during LLS, leading to fewer changes to ΔHbO.

Estimation of the Differential Pathlength Factor for Human Skin Using Monte Carlo Simulations.

Near-infrared technology is an emerging non-invasive technique utilized for various medical applications. Recently, there have been many attempts to utilize NIR technology for the continues monitoring of blood glucose levels through the skin. Different approaches and designs have been proposed for non-invasive blood glucose measurements. Light photons penetrating the skin can undergo multiple scattering events, and the actual optical pathlength becomes larger than the source-to-detector separation (optode spacing) in the reflection-mode configuration. Thus, the differential pathlength factor (DPF) must be incorporated into the modified Beer-Lambert law. The accurate estimation of the DPF values will lead to an accurate quantification of the physiological variations within the tissue. In this work, the aim was to systematically estimate the DPF for human skin for a range of source-to-detector separations and wavelengths. The Monte Carlo (MC) method was utilized to mimic the different layers of human skin with different optical properties and blood and water volume fractions. This work could help improve the accuracy of the near-infrared technique in the measurement of physiological variations within skin tissue.

Mapping the Distribution of Melanin Concentration in Different Fitzpatrick Skin Types Using Hyperspectral Imaging Technique.

Measuring skin melanin concentration in order to assess skin phototype according to Fitzpatrick's classification is a constant research goal. In this study, a new approach for assessing skin melanin concentration based on hyperspectral imaging combined with an appropriate analytical model that exploits specific spectral bands to generate maps of melanin content distribution on different Fitzpatrick skin phototypes is presented. Hyperspectral images from the proximal inner side of the forearms of 51 young volunteers covering the first four classes of Fitzpatrick's phototypes were acquired using a hyperspectral imaging system. The images were analyzed using a modified Beer-Lambert law that segregates the contribution of melanin from the other constituents to the skin absorption spectrum. The performance of the model was evaluated using the coefficient of determination (r-squared). The results revealed that the approach proposed in this study generated accurate melanin concentration distribution maps that allowed a correct classification of skin phototype. In conclusion, the proposed approach for assessing skin melanin concentration proved to be very reliable for classifying skin phototypes, and, as it provides maps that are easily read, it has the advantage of a possible extension of its applications to other research concerning skin pigmentation.

Real time detection of cognitive load using fNIRS: A deep learning approach

Functional near infrared spectroscopy (fNIRS) is a non-invasive tool for monitoring functional brain activation that records changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations. fNIRS is well accepted in the cognitive study where the signals are intended to measure cognitive load in the human brain. Concentration changes in HbO and HbR help in classifying the cognitive states of human brain. There are several machine learning classification techniques to distinguish different cognitive states. Some conventional machine learning methods, which are easier to implement, undergo a complex processing phase before training the network and also suffer from low accuracy due to inappropriate data preprocessing. Deep learning based convolutional neural network (CNN) having automatic feature engineering capability plays a very important role in efficiently classifying different cognitive states. The present work uses two open-access datasets on fNIRS signal. The datasets are taken for two cognitive states: mental task (MT) and resting state or baseline task (BL). The concentration changes of HbO and HbR are computed using the modified Beer–Lambert law. The band-pass filter is used to remove additional noise from the signals. Here, topographical brain images are generated from the data of 2 s window with 1 s overlapping for both HbO and HbR. Global normalization is applied to the filtered data for better visualization of the images. The brain images are fed to the proposed CNN model in order to classify them into MT or BL. The accuracy of the classification and the comparative study shows the superiority of the proposed model over two existing models.

Characterization of forehead blood flow bias on NIRS signals during neural activation with a verbal fluency task

The major problem of near-infrared spectroscopy (NIRS) for brain activity measurement during verbal fluency task is the overlapping forehead scalp blood flow (FBF) on the target cerebral blood flow (CBF). There could be among-individual differences in the influence of FBF on CBF. We investigated effects of FBF on CBF by comparing signals obtained through a laser Doppler flowmeter (LDF) and NIRS using the modified Beer-Lambert Law (MBLL). Among 25 healthy individuals, 7 participants showed a strong correlation between LDF and NIRS signals (rs >0.500). There were no significant differences according to age or sex. Subsequently, we applied the hemodynamic separation method to the values calculated using the MBLL (Δ[oxy-Hb]M): to separate the concentration of oxygenated hemoglobin in the forehead (Δ[oxy-Hb]F) and cerebral cortex (Δ[oxy-Hb]C). First, we found that the influence of Δ[oxy-Hb]F on Δ[oxy-Hb]C in the high rs group was almost twice as large as that in the low rs group. Second, presence of sex and age differences in the influence of Δ[oxy-Hb]F on Δ[oxy-Hb]C were suggested. Based on the results, we discuss the factors affecting FBF and the resulting variations in NIRS signals.

Three-dimensional representation of triple spectral line imaging data as an option for noncontact skin diagnostics

.Significance Skin malformations in dermatology are mostly evaluated subjectively, based on a doctor’s experience and visual perception; an option for objective quantitative skin assessment is camera-based spectrally selective diagnostics. Multispectral imaging is a technique capable to provide information about concentrations of the absorbing chromophores and their distribution over the malformation in a noncontact way. Conversion of spectral images into distribution maps of chromophores can be performed by means of the modified Beer–Lambert law. However, such distribution maps represent only single specific cases, therefore, some extensive method for data comparison is needed.Aim This study aims to develop a more informative approach for identification and characterization of skin malformations using three-dimensional (3D) representation of triple spectral line imaging data.Approach The 3D-representation method is experimentally tested on eight different skin pathology types, including both benign and malignant pathologies; an imaging device ensuring uniform three laser line (448, 532, and 659 nm) illumination is used. Three spectral line images are extracted from a single snapshot RGB image data, with subsequent calculation of attenuation coefficients for each working wavelength at every image pixel and represented as 3D graphs. Skin chromophore content variations in malformations are represented in a similar way.ResultsClinical measurement results for 99 skin pathologies, including basal cell carcinomas, melanoma, dermal nevi, combined nevi, junctional nevi, blue nevi, seborrheic keratosis, and hemangiomas. They are presented as 3D spectral attenuation maps exhibiting specific individual features for each group of pathologies. Along with intensity attenuation maps, 3D maps for content variations of three main skin chromophores (melanin, oxyhemoglobin, and deoxyhemoglobin), calculated in frame of a model based on modified Beer–Lambert law, are also presented. Advantages and disadvantages of the proposed data representation method are discussed.ConclusionsThe described 3D-representation method of triple spectral line imaging data shows promising potential for objective quantitative noncontact diagnosis of skin pathologies.

Simulated Annealing-Based Wavelength Selection for Robust Tissue Oxygenation Estimation Powered by the Extended Modified Lambert–Beer Law

In this paper, we present a set of algorithms to enable the development of inexpensive hyperspectral sensors capable of estimating tissue oxygenation for wound monitoring. Estimation is conducted using the extended modified Lambert–Beer law, which has previously been proven robust to differences in melanin concentration. We introduce a novel wavelength selection algorithm that enables the estimation to be performed with high accuracy using only a small number (5–10) of wavelengths. Validation performed with Monte Carlo simulation data resulted in prediction errors &lt;1%, with no significant differences among various skin types, for as few as five wavelengths under conditions representing both high precision instrumentation and more cost-effective sensors designed with inexpensive LEDs and/or filters. Validation with in vivo data collected from an occlusion study with 13 Asian volunteers showed statistically significant separation between the estimates for the at-rest and arterial occlusion states. Additional stability testing proved the proposed algorithms to be robust to small changes in the selected wavelengths as may occur in a real LED due to manufacturing tolerances and temperature fluctuations. This work concluded that the development of an inexpensive hyperspectral device for wound monitoring in all skin types is feasible using just a small number of wavelengths.

Signal Processing for Hybrid BCI Signals

The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are not robust for real-time application because of poor spatial or temporal resolution. To overcome this, a hybrid BCI technology that combines two acquisition systems has been introduced. In this work, we have discussed a preprocessing pipeline for enhancing brain signals acquired from fNIRS (functional Near Infrared Spectroscopy) and EEG (Electroencephalography). The data consists of brain signals for four tasks – Right/Left hand gripping and Right/Left arm raising. The EEG (brain activity) data were filtered using a bandpass filter to obtain the activity of mu (7-13 Hz) and beta (13-30 Hz) rhythm. The Oxy-haemoglobin and Deoxy-haemoglobin (HbO and HbR) concentration of the fNIRS signal was obtained with Modified Beer Lambert Law (MBLL). Both signals were filtered using a fifth-order Butterworth band pass filter and the performance of the filter is compared theoretically with the estimated signal-to-noise ratio. These results can be used further to improve feature extraction and classification accuracy of the signal.

Influence of the Signal-To-Noise Ratio on Variance of Chromophore Concentration Quantification in Broadband Near-Infrared Spectroscopy

This study presented a theoretical or analytical approach to quantify how the signal-to-noise ratio (SNR) of a near infrared spectroscopy (NIRS) device influences the accuracy on calculated changes of oxy-hemoglobin (Δ[HbO]), deoxy-hemoglobin (Δ[HHb]), and oxidized cytochrome c oxidase (Δ[oxCCO]). In theory, all NIRS experimental measurements include variations due to thermal or electrical noise, drifts, and disturbance of the device. Since the computed concentration results are highly associated with device-driven variations, in this study, we applied the error propagation analysis to compute the variability or variance of Δ[HbO], Δ[HHb], and Δ[oxCCO] depending on the system SNR. The quantitative expressions of variance or standard deviations of changes in chromophore concentrations were derived based on the error propagation analysis and the modified Beer-Lambert law. In order to compare and confirm the derived variances versus those from the actual measurements, we conducted two sets of broadband NIRS (bbNIRS) measurements using a solid tissue phantom and the human forearm. A Monte Carlo framework was also executed to simulate the bbNIRS data under two physiological conditions for further confirmation of the theoretical analysis. Finally, the confirmed expression for error propagation was utilized for quantitative analyses to guide optimal selections of wavelength ranges and different wavelength combinations for minimal variances of Δ[HbO], Δ[HHb], and Δ[oxCCO] in actual experiments.