Infrared Spectroscopy Signals Research Articles

C-STIM: Protocol for a randomized, single-blind, crossover study of cerebellar repetitive transcranial magnetic stimulation (rTMS) for postural instability in people with progressive supranuclear palsy (PSP)

BackgroundMethods for modulating the cerebellum with transcranial magnetic stimulation (TMS) are well established, and preliminary data from our group and others has shown evidence of transient improvements in balance after cerebellar repetitive transcranial magnetic stimulation (rTMS) in progressive suprancuclear palsy (PSP). This study examines extensive posturography measures before and after 10 sessions of cerebellar rTMS and sham TMS in PSP. MethodsThirty subjects with PSP and postural instability will undergo cerebellar active and sham rTMS in a single-blind, crossover design with a randomized order of a 10-day intervention. Primary outcomes will be changes in sway area and medio-lateral range of sway with eyes open while standing on a stationary force-plate, and safety, tolerability, and blindedness. Secondary outcomes will include posturography and gait analysis with body-worn, triaxial inertial sensors, clinical balance scales and questionnaires, and a bedside test of vestibular function. Exploratory outcomes are changes in functional near infrared spectroscopy (fNIRS) signal over the prefrontal, supplementary motor, and primary motor cortices while standing and walking, and speech samples for future analysis. DiscussionThe C-STIM crossover intervention study adds a longer duration of stimulation and extensive posturography measures to more finely measure the improvements in balance and exploratory functional near-infrared spectroscopy (fNIRS) over the prefronal, supplementary motor, and primary motor cortices during balance assessments before and after 10 sessions of cerebellar rTMS and 10 sessions of sham cerebellar TMS. This project will improve our understanding of the importance of the cerebellum for control of postural stability in PSP.

Performance Assessment of Heartbeat Detection Algorithms on Photoplethysmograph and Functional NearInfrared Spectroscopy Signals

With wearable sensors, the acquisition of physiological signals has become affordable and feasible in everyday life. Specifically, Photoplethysmography (PPG), being a low-cost and highly portable technology, has attracted notable interest for measuring and diagnosing cardiac activity, one of the most important physiological and autonomic indicators. In addition to the technological development, several specific signal-processing algorithms have been designed to enable reliable detection of heartbeats and cope with the lower quality of the signals. In this study, we compare three heartbeat detection algorithms: Derivative-Based Detection (DBD), Recursive Combinatorial Optimization (RCO), and Multi-Scale Peak and Trough Detection (MSPTD). In particular, we considered signals from two datasets, namely, the PPG-DALIA dataset (N = 15) and the FANTASIA dataset (N = 20) which differ in terms of signal characteristics (sampling frequency and length) and type of acquisition devices (wearable and medical-grade). The comparison is performed both in terms of heartbeat detection performance and computational workload required to execute the algorithms. Finally, we explore the applicability of these algorithms on the cardiac component obtained from functional Near InfraRed Spectroscopy signals (fNIRS).The results indicate that, while the MSPTD algorithm achieves a higher F1 score in cases that involve body movements, such as cycling (MSPTD: Mean = 74.7, SD = 14.4; DBD: Mean = 54.4, SD = 21.0; DBD + RCO: Mean = 49.5, SD = 22.9) and walking up and down the stairs (MSPTD: Mean = 62.9, SD = 12.2; DBD: Mean = 50.5, SD = 11.9; DBD + RCO: Mean = 45.0, SD = 14.0), for all other activities the three algorithms perform similarly. In terms of computational complexity, the computation time of the MSPTD algorithm appears to grow exponentially with the signal sampling frequency, thus requiring longer computation times in the case of high-sampling frequency signals, where the usage of the DBD and RCO algorithms might be preferable. All three algorithms appear to be appropriate candidates for exploring the applicability of heartbeat detection on fNIRS data.

Benchmarking framework for machine learning classification from fNIRS data.

While efforts to establish best practices with functional near infrared spectroscopy (fNIRS) signal processing have been published, there are still no community standards for applying machine learning to fNIRS data. Moreover, the lack of open source benchmarks and standard expectations for reporting means that published works often claim high generalisation capabilities, but with poor practices or missing details in the paper. These issues make it hard to evaluate the performance of models when it comes to choosing them for brain-computer interfaces. We present an open-source benchmarking framework, BenchNIRS, to establish a best practice machine learning methodology to evaluate models applied to fNIRS data, using five open access datasets for brain-computer interface (BCI) applications. The BenchNIRS framework, using a robust methodology with nested cross-validation, enables researchers to optimise models and evaluate them without bias. The framework also enables us to produce useful metrics and figures to detail the performance of new models for comparison. To demonstrate the utility of the framework, we present a benchmarking of six baseline models [linear discriminant analysis (LDA), support-vector machine (SVM), k-nearest neighbours (kNN), artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM)] on the five datasets and investigate the influence of different factors on the classification performance, including: number of training examples and size of the time window of each fNIRS sample used for classification. We also present results with a sliding window as opposed to simple classification of epochs, and with a personalised approach (within subject data classification) as opposed to a generalised approach (unseen subject data classification). Results show that the performance is typically lower than the scores often reported in literature, and without great differences between models, highlighting that predicting unseen data remains a difficult task. Our benchmarking framework provides future authors, who are achieving significant high classification scores, with a tool to demonstrate the advances in a comparable way. To complement our framework, we contribute a set of recommendations for methodology decisions and writing papers, when applying machine learning to fNIRS data.

Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance during Motor Training.

As electroencephalography (EEG) is nonlinear and nonstationary in nature, an imperative challenge for brain-computer interfaces (BCIs) is to construct a robust classifier that can survive for a long time and monitor the brain state stably. To this end, this research aims to improve BCI performance by incorporation of electroencephalographic and cerebral hemodynamic patterns. A motor imagery (MI)-BCI based visual-haptic neurofeedback training (NFT) experiment was designed with sixteen participants. EEG and functional near infrared spectroscopy (fNIRS) signals were simultaneously recorded before and after this transient NFT. Cortical activation was significantly improved after repeated and continuous NFT through time-frequency and topological analysis. A classifier calibration strategy, weighted EEG-fNIRS patterns (WENP), was proposed, in which elementary classifiers were constructed by using both the EEG and fNIRS information and then integrated into a strong classifier with their independent accuracy-based weight assessment. The results revealed that the classifier constructed on integrating EEG and fNIRS patterns was significantly superior to that only with independent information (~10% and ~18% improvement respectively), reaching ~89% in mean classification accuracy. The WENP is a classifier calibration strategy that can effectively improve the performance of the MI-BCI and could also be used to other BCI paradigms. These findings validate that our proposed methods are feasible and promising for optimizing conventional motor training methods and clinical rehabilitation.

EFFECT OF SIMULATED SPACE CONDITIONS ON FUNCTIONAL CONNECTIVITY

Long duration spaceflight missions can affect the cognitive and behavioral activities of astronauts due to changes in gravity. The microgravity significantly impacts the central nervous system physiology which causes the degradation in the performance and lead to potential risk in the space exploration. The aim of this study was to evaluate functional connectivity at simulated space conditions using an unloading harness system to mimic the body-weight distribution related to Earth, Mars, and International Space Station. A unity model with six directional arrows to imagine six different motor imagery tasks associated with arms and legs were designed for the Oculus Rift S virtual reality headset for testing. An Electroencephalogram (EEG) and functional near infrared spectroscopy (fNIRS) signals were recorded from 10 participants in the distributed weight conditions related to Earth, Mars, and International Space station using the g.Nautilus fNIRS system at sampling rate of 500 Hz. The magnitude squared coherence were estimated from left vs right hemisphere of the brain that represents functional connectivity. The EEG coherence was the higher which shows the strong functional connectivity and fNIRS coherence was lower shows weak functional connectivity between left vs right hemisphere of the brain, during all the tasks and trials irrespective of the simulated space conditions. Further analysis of functional connectivity needed between the intra-regions of the brain.

Reproducible Inter-Personal Brain Coupling Measurements in Hyperscanning Settings With functional Near Infra-Red Spectroscopy.

Despite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temporal variations between signals by allowing for misalignments up to a maximum lag. Starting from the observed effect sizes, computed in terms of Cohen's d, the power analysis indicated that a high sample size ([Formula: see text]) would be required to detect significant brain-coupling. We therefore discuss the need for specialized statistical approaches and propose bootstrap as an alternative method to avoid over-penalizing the results. In our settings, and based on bootstrap analyses, Cross Correlation and Dynamic Time Warping outperform Mutual Information and Wavelet Coherence for all considered maximum lags, with reproducible results. These results highlight the need to set specific guidelines as the high degree of customization of the signal processing procedures prevents the comparability between studies, their reproducibility and, ultimately, undermines the possibility of extracting new knowledge.

Near-infrared optical spectroscopy for pancreas shrinkage estimation with multi synchrosqueezing transform and multivariate regression model.

In this article, we proposed a method to estimate pancreas shrinkage with pancreas β cell insulin secretion. The β cells in the pancreas secrete insulin and digestive enzymes after food consumption. Conventionally, the pancreas structure estimation is done with magnetic resonance imaging (MRI) and ultrasound imaging techniques. However, the structure of the pancreas changes due to islet cell death. The presence of islet cells is detected through near infrared (NIR) spectroscopy signal acquired from the epigastric region (pancreas) of the abdomen. Subsequently, the NIR spectroscopy signal from the pancreas is analyzed with multi synchrosqueezing transform (MSST); whereas, the β cell insulin secretion varies for diabetic and nondiabetic persons. The existence of β cell and insulin secretion correlates with Root Mean Square (RMS) and kurtosis via a multivariate regression model to evaluate pancreas shrinkage. In terms of numerical results, NIR spectroscopy signal from the pancreas was obtained for about 20 nondiabetic and 20 diabetic persons. The pancreas shrinkage was estimated with 88% accuracy. The results are validated with MRI pancreas images for earlier detection of the apoptotic pancreas. The pancreas shrinkage causes lower insulin emission and unpredictable blood glucose in diabetic patients. Analysis of NIR spectroscopy signals of the pancreas with MSST was done to obtain higher-order and lower-order frequency components.

Decoding different working memory states during an operation span task from prefrontal fNIRS signals

We propose an effective and practical decoding method of different mental states for potential applications for the design of brain-computer interfaces, prediction of cognitive behaviour, and investigation of cognitive mechanism. Functional near infrared spectroscopy (fNIRS) signals that interrogated the prefrontal and parietal cortices and were evaluated by generalized linear model were recorded when nineteen healthy adults performed the operation span (OSPAN) task. The oxygenated hemoglobin changes during OSPAN, response, and rest periods were classified with a support vector machine (SVM). The relevance vector regression algorithm was utilized for prediction of cognitive performance based on multidomain features of fNIRS signals from the OSPAN task. We acquired decent classification accuracies for OSPAN vs. response (above 91.2%) and for OSPAN vs. rest (above 94.7%). Eight of the ten cognitive testing scores could be predicted from the combination of OSPAN and response features, which indicated the brain hemodynamic responses contain meaningful information suitable for predicting cognitive performance.

Modulating and restoring inter-muscular coordination in stroke patients using two-dimensional myoelectric computer interface: a cross-sectional and longitudinal study

Objective. Traditional training focuses on improving the motor function level of the limbs or joint levels, while inter-muscular coordination plays an important role in fine motor control and is often overlooked. The purpose of this study was to investigate the changes in inter-muscular coordination induced by the myoelectric-controlled interface (MCI) and the therapeutic effects of MCI-based inter-muscular coordination training on stroke patients. Approach. Eleven stroke patients, twenty young subjects and thirteen age-matched subjects were recruited to determine the dimensionality effect of MCI on inter-muscular coordination in the evaluation test. In addition, a stroke patient participated in a 20-day training session to test the therapeutic effects as a case study analysis in the training test. In these two tests, all subjects performed tracking tasks by flexing/extending their elbows according to the biofeedback from one-dimensional and two-dimensional (2D) MCI. Meanwhile, the electromyography and functional near infrared spectroscopy signals were recorded simultaneously to reflect the muscle and cortical activations. Main results. In all groups, as the MCI dimensionality increased, the antagonist activation decreased significantly, while the involvement in prefrontal cortex and primary motor cortex increased significantly. A significant reduction in muscle activation and an increase in cortical activation were found in the stroke patient, which might be due to a progressive normalization of patient after the training. Significance. These findings suggested that 2D MCI could be an effective tool to directly modulate inter-muscular coordination for stroke patients. Inter-muscular coordination training may restore the ability to coordinate agonist-antagonist muscle of stroke patient and this improvement may be accompanied by cortical reorganization.

Signal quality index: an algorithm for quantitative assessment of functional near infrared spectroscopy signal quality.

We propose the signal quality index (SQI) algorithm as a novel tool for quantitatively assessing the functional near infrared spectroscopy (fNIRS) signal quality in a numeric scale from 1 (very low quality) to 5 (very high quality). The algorithm comprises two preprocessing steps followed by three consecutive rating stages. The results on a dataset annotated by independent fNIRS experts showed SQI performed significantly better (p<0.05) than PHOEBE (placing headgear optodes efficiently before experimentation) and SCI (scalp coupling index), two existing algorithms, in both quantitatively rating and binary classifying the fNIRS signal quality. Employment of the proposed algorithm to estimate the signal quality before processing the fNIRS signals increases certainty in the interpretations.

Classification of autism spectrum disorder based on sample entropy of spontaneous functional near infra-red spectroscopy signal

ObjectivesTo assess the possibility of distinguishing autism spectrum disorder (ASD) based on the characteristic of spontaneous hemodynamic fluctuations and to explore the location of abnormality in the brain. MethodsUsing the sample entropy (SampEn) of functional near-infrared spectroscopy (fNIRS) from bilateral inferior frontal gyrus (IFG) and temporal cortex (TC) on 25 children with ASD and 22 typical development (TD) children, the pattern of mind-wandering was assessed. With the SampEn as feature variables, a machine learning classifier was applied to mark ASD and locate the abnormal area in the brain. ResultsThe SampEn was generally lower for ASD than TD, indicating the fNIRS series from ASD was unstable, had low fluctuation, and high self-similarity. The classification between ASD and TD could reach 97.6% in accuracy. ConclusionsThe SampEn of fNIRS could accurately distinguish ASD. The abnormality in terms of the SampEn occurs more frequently in IFG than TC, and more frequently in the left than in the right hemisphere. SignificanceThe results of this study may help to understand the cortical mechanism of ASD and provide a fNIRS-based diagnosis for ASD.

Neuromuscular Control of the Agonist–Antagonist Muscle Coordination Affected by Visual Dimension: An EMG-fNIRS Study

Motor planning can enable integration of sensory input to generate motor execution. However, how the brain analyses visual information and modulates its signals to the muscle have been not well studied in human. The aim of this study was to investigate the dimensionality effect of myoelectric-controlled interface (MCI) on motor planning and motor execution during elbow tracking movements. Twenty right-handed healthy subjects were recruited to complete tracking tasks by modulating their biceps and triceps activation within one-dimensional and two-dimensional MCI. The electromyography (EMG) signals of the biceps and triceps was recorded to calculate the normalized muscle activation, while the functional near infrared spectroscopy (fNIRS) signals of the prefrontal cortex (PFC) and bilateral motor cortex (MC) were also collected to gain the brain activation simultaneously. The results showed that the activation of antagonist muscle was significant lower within two-dimensional MCI than that within one-dimensional MCI at the muscle level. At the brain level, it was found an obvious higher activation in the PFC and the left MC within two-dimensional MCI than that within one-dimensional MCI. The current EMG-fNIRS study confirmed that visual feedback can influence motor planning and motor execution, and the PFC and bilateral MC are the likely targeted sites of the proactive inhibition of the antagonist muscle. This study adds a new perspective to possible visual regulation of neuromuscular control, which might be an effective rehabilitation method to improve abnormal muscle coordination in the clinic.

Non-invasive FTIR study of mortar model samples: comparison among innovative and traditional techniques

Mortars are important building materials that are produced using different raw materials for the binder and for the aggregate; the latter is usually composed of several different mineral phases, or ceramic (cocciopesto) or rocks fragments. Traditional local recipes often include a vast range of organic and inorganic additives, but unfortunately no precise descriptions of the recipes have been reported in historical sources. Knowledge of their composition could be extremely significant in terms of information on past technology and improvements in today’s conservation work. In recent years, non-invasive portable instruments have been increasingly used in conservation science, particularly for the analysis of paintings, but little research focuses on historical building materials. In this study, the performance of non-invasive external reflection infrared spectroscopy (ER-FTIR) is tested on model samples, which are prepared according to past recipes collected from historical manuals, and using conservation best practices. The results are discussed and compared with traditional analytical techniques, such as bench top transmission and attenuated total reflectance (ATR) infrared spectroscopy, combined with powder X-ray diffraction (XRD). The influence of surface roughness on reflection infrared spectroscopy signals is also considered and investigated.

Predicting the contents of polysaccharides and its monosugars in Dendrobium huoshanense by partial least squares regression model using attenuated total reflectance Fourier transform infrared spectroscopy

An attenuated total reflectance Fourier transform infrared spectroscopy method combined with partial least square regression was established to rapidly quantify the total polysaccharide and its major monosugars in Dendrobium huoshanense. The results showed that the optimal predictive methods were the models established by the infrared spectroscopy files pretreated by standard normal transformation combined with the second derivative; the accuracy of the models selecting wavelength regions based on the assignments of Fourier transform infrared spectroscopy signals of polysaccharide was obviously higher than the models selecting wavelength regions suggested by Thermofisher Quantity Analyst software and other possible wavelength region selecting modes. The external validation and the complete external validation confirmed the robustness and reliability of the developed attenuated total reflectance Fourier transform infrared model. Our study might provide an efficient technique tool for the rapid, green, low-cost, and nondestructive quantification of the total polysaccharide and the main monosaccharides in Dendrobium huoshanense and other rich-in-polysaccharide plant food or medicines.

Evaluation of classification performance of functional near infrared spectroscopy signals during movement execution for developing a brain–computer interface application using optimal channels

The objective of brain–computer interface application is to give a new lease of life to motor-impaired people by enabling them to communicate with the external world purely based on thoughts. Functional near infrared spectroscopy technology uses light to detect brain activations related to the motor activity performed by the subjects and an emerging modality in the brain–computer interface field. The study aims at comparing haemodynamic responses during upper limb movement tasks and resting condition over 14 channels placed equally on right and left hemispheres. Channel selection based on statistical methodology has been introduced to optimally select those channels that give statistical differences between the motor tasks. Besides using commonly used features such as mean, variance and peak of oxygenated haemoglobin signal, two other features, namely difference between mean concentration changes of oxygenated haemoglobin and deoxygenated haemoglobin and difference between mean activation level of oxygenated haemoglobin during the task condition and mean activation level of oxygenated haemoglobin during the resting state, are investigated. Their effect on the artificial neural network classifier accuracy over the selected channels is analysed. The proposed channel selection strategy is compared with the channel-averaging approach, which is one of the conventional channel selection methods. Also, performance of these methods is compared when all 14 channels are considered for classification. Classification of functional near infrared spectroscopy signals corresponding to motor tasks and rest condition gave accuracy values of (87.25 ± 8.66)% over 14 channels, (83.81 ± 5.97)% over channels selected using channel-averaging method and (84.71 ± 4.97)% over the selected subset of four channels. Hence, the results in terms of classification accuracies are promising and demonstrate the prospect of developing functional near infrared spectroscopy-based brain–computer interface application. Also, the analysis carried out in the study showed that the upper limb motor tasks evoked different haemodynamic responses in the reduced set of channels located over centro-parietal regions of the brain.

Classification of Mental Stress Levels by Analyzing fNIRS Signal Using Linear and Non-linear Features

Background: Mental stress is known as one of the main influential factors in development of different diseases including heart attack and stroke. Thus, quantification of stress level can be very important in preventing many diseases and in human health. Methods: The prefrontal cortex is involved in body regulation in response to stress. In this research, functional near infrared spectroscopy (fNIRS) signals were recorded from FP2 position in the international electroencephalographic 10–20 system during a stressful mental arithmetic task to be calculated within a limited period of time. After extracting the brain’s hemodynamic response from fNIRS signal, different linear and nonlinear features were extracted from the signal which are then used for stress levels classification both individually and in combination. Results: In this study, the maximum accuracy of 88.72% was achieved in classification between high and low stress levels, and 96.92% was obtained for the stress and rest states. Conclusion: Our results showed that using the proposed linear and nonlinear features it is possible to effectively classify stress levels from fNIRS signals recorded from only one site in the prefrontal cortex. Comparing to other methods, it is shown that the proposed algorithm outperforms other previously reported methods using the nonlinear features extracted from the fNIRS signal. These results clearly show the potential of fNIRS signal as a useful tool for early diagnosis and quantify stress.

Robust functional near infrared spectroscopy denoising using multiple wavelet shrinkage based on a hemodynamic response model

Functional near infrared spectroscopy can measure hemodynamic signals, and the results are similar to functional magnetic resonance imaging of blood-oxygen-level-dependent signals. Thus, functional near infrared spectroscopy can be employed to investigate brain activity by measuring the absorption of near infrared light through an intact skull. Recently, a general linear model, which is a standard method for functional magnetic resonance imaging, was applied to functional near infrared spectroscopy imaging analysis. However, the general linear model fails when functional near infrared spectroscopy signals retain noise, such as that caused by the subject's movement during measurement. Although wavelet-based denoising and hemodynamic response function smoothing are popular denoising methods for functional near infrared spectroscopy signals, these methods do not exhibit impressive performances for very noisy environments and a specific class of noise. Thus, this paper proposes a new denoising algorithm that uses multiple wavelet shrinkage and a multiple threshold function based on a hemodynamic response model. Through the experiments, the performance of the proposed algorithm is verified using graphic results and objective indexes, and it is compared with existing denoising algorithms.

Maturity Related Differences in Systemic and Localized Fatigue Thresholds amongst Adolescent Male Athletes

Fatigue thresholds (FTs) can be determined via systemic pulmonary measurements such as oxygen consumption (VO2), carbon dioxide production (VCO2), and ventilation (VE) or through localized muscular measurements such as neuromuscular activation and oxygenation. Systemic and localized FT measures are non‐invasive techniques, which make them ideal tests for adolescents. During puberty, a gradual shift occurs in the metabolic response to exercise from predominantly aerobic metabolism to a balance between aerobic and anaerobic metabolic systems. However, limited research has examined the maturity related differences in systemic and localized FTs amongst adolescent male athletes.PURPOSETo examine the maturity related differences in systemic pulmonary and localized neuromuscular and oxygenation FTs amongst adolescent male athletes.METHODSTwenty‐six adolescent male athletes (age 11–17 yrs) volunteered with verbal assent and written parental consent to complete two incremental ramp exercise tests on a cycle ergometer separated by at least 48 hours. Three systemic FTs, gas exchange threshold (GET), ventilatory threshold (VT), and respiratory compensation point (RCP) were calculated from VCO2 and VE. Three localized FTs, neuromuscular fatigue threshold (NFT), deoxyhemoglobin breakpoint (HHbBP), and oxygenation breakpoint (OxBP) were calculated from electromyography and near infrared spectroscopy signals from the right vastus lateralis. Data from the second test were plotted versus VO2 and 30‐second moving averages were calculated. All FTs were determined using the maximal distance method and expressed relative to VO2max. All participants were separated according to the number of years from peak height velocity (PHV), an estimation of somatic maturity status, into PRE‐ (&lt;−1yr), PERI‐ (between −1 to +1yr) and POST‐ (&gt;+1yr) PHV groups. Differences between maturational groups were assessed with magnitude based inferences.RESULTSGroup means and differences between groups are presented in Table 1. All systemic and localized FTs occurred at Very Likely or Likely greater relative intensities in PRE compared to POST. Only systemic FTs occurred at Very Likely or Likely greater relative intensities in PERI compared to POST, with Unclear differences for all localized FTs. When comparing PRE and PERI, only HHbBP and OxBP occurred at Likely greater relative intensities in PRE, with Unclear differences for all other FTs.CONCLUSIONSSystemic FTs occurred at lower relative intensities in POST when compared to PRE and PERI; while localized FTs were not different between PERI and POST. Furthermore, systemic FTs and NFT did not occur at different relative intensities in PRE and PERI; however, HHbBP and OxBP occurred at greater relative intensities in PRE. Therefore, maturity related differences in FTs do exist amongst adolescent males, which may represent the change in the metabolic response to exercise that occurs with maturation. The disparity in the maturity related differences between systemic and localized FTs may indicate that maturation affects the muscular response to exercise prior to affecting the pulmonary response. Mean ± SD in fatigue thresholds (%VO2max) for each maturational group. Fatigue Threshold PRE PERI POST Systemic Pulmonary GET 60.86 ± 5.43* 61.25 ± 5.33* 52.92 ± 5.73 VT 68.64 ± 1.59* 68.05 ± 2.96# 63.70 ± 5.86 RCP 82.33 ± 4.96* 82.66 ± 4.12* 76.11 ± 6.34 Localized Muscular NFT 80.26 ± 4.20# 77.20 ± 5.68 74.61 ± 8.14 HHbBP 80.02 ± 6.83#$ 72.89 ± 10.50 74.68 ± 7.97 OxBP 64.07 ± 10.55#$ 55.54 ± 8.75 55.51 ± 10.73 Very Likely greater than POST; Likely greater than POST; Likely greater than PERI

Processing Functional Near Infrared Spectroscopy Signal with a Kalman Filter to Assess Working Memory during Simulated Flight

Working memory (WM) is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor WM as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering, and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces (BCI). We used functional near infrared spectroscopy as it has been already successfully tested to measure WM capacity in complex environment with air traffic controllers (ATC), pilots, or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with nine participants involving a basic WM task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with ATC instructions (two levels of difficulty). The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence (MACD) filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in WM. In addition, the use of a Kalman filter increased the performance of the classification of WM levels based on brain signal. The results suggest that Kalman filter is a suitable approach for real-time improvement of near infrared spectroscopy signal in ecological situations and the development of BCI.

Effective channels in classification and functional connectivity pattern of prefrontal cortex by functional near infrared spectroscopy signals

In this paper, we apply support vector machine (SVM) based classification of functional near-infrared spectroscopy (fNIRS) which is non-invasive monitoring of human brain function by measuring the changes in the concentration of oxyhemoglobin and deoxyhemoglobin. Data collected from 11 healthy volunteers and 16 schizophrenia subjects. Signals were first preprocessed and decomposed by using discrete wavelet transform DWT to eliminate systemic physiological interference. A preliminary analysis based on Genetic Algorithm (GA) favored eight channels of the reconstructed fNIRS signals for further analysis. Energy in these 8 reconstructed signals was computed and used for classification of signals. SVM based classifier was employed to diagnosis schizophrenia. The results show the promising classification accuracy of nearly 84% in detection of schizophrenia from healthy subjects. The major finding of this study is that selected channels were able to identify differences in functional connectivity patterns of prefrontal cortex (PFC) elicited by Stroop task.