µ-STAR: A novel framework for spatio-temporal M/EEG source imaging optimized by microstates.

Source imaging of Electroencephalography (EEG) and Magnetoencephalography (MEG) provides a noninvasive way of monitoring brain activities with high spatial and temporal resolution. In order to address this highly ill-posed problem, conventional source imaging models adopted spatio-temporal constraints that assume spatial stability of the source activities, neglecting the transient characteristics of M/EEG. In this work, a novel source imaging method µ-STAR that includes a microstate analysis and a spatio-temporal Bayesian model was introduced to address this problem. Specifically, the microstate analysis was applied to achieve automatic determination of time window length with quasi-stable source activity pattern for optimal reconstruction of source dynamics. Then a user-specific spatial prior and data-driven temporal basis functions were utilized to characterize the spatio-temporal information of sources within each state. The solution of the source reconstruction was obtained through a computationally efficient algorithm based upon variational Bayesian and convex analysis. The performance of the µ-STAR was first assessed through numerical simulations, where we found that the determination and inclusion of optimal temporal length in the spatio-temporal prior significantly improved the performance of source reconstruction. More importantly, the µ-STAR model achieved robust performance under various settings (i.e., source numbers/areas, SNR levels, and source depth) with fast convergence speed compared with five widely-used benchmark models (including wMNE, STV, SBL, BESTIES, & SI-STBF). Additional validations on real data were then performed on two publicly-available datasets (including block-design face-processing ERP and continuous resting-state EEG). The reconstructed source activities exhibited spatial and temporal neurophysiologically plausible results consistent with previously-revealed neural substrates, thereby further proving the feasibility of the µ-STAR model for source imaging in various applications.

A new Einstein coefficient method for mesopause-lower thermosphere atmosphere temperature retrieval under a non-local thermal equilibrium situation.

The mesopause-lower thermosphere (MLT) region is an important spatial region in the Earth's atmosphere, making it a valuable area to investigate the temperature variations. Kirchhoff's law fails with the altitude increase due to the non-local thermal equilibrium effect, resulting in an increase in the error of the method to retrieve the atmospheric temperature in the MLT region using the A-band spectral line intensity. In the non-LTE state, the temperature retrieval method based on the Einstein coefficients is proposed to retrieve atmospheric temperature in the 92-140 km height range using the airglow radiation intensity images obtained from the Michelson Interferometer for global high-resolution thermospheric imaging (MIGHTI) measurements. Results show that the temperature deviation of the two-channel combinations does not exceed 15 K in the altitude range of 92-120 km. This deviation increases up to 45 K when the altitude is in the range of 120-140 km due to the influence of the N2 airglow spectrum. The two-channel combinations self-consistency is increased by 85 K compared with the temperature obtained using the spectral line intensity retrieval. Additionally, the comparison of the retrieval results with the spectral line intensity method and the comparison with the atmospheric chemistry experiment Fourier transform spectrometer (ACE-FTS) temperature measurement data shows that the Einstein coefficient method is significantly more rational and accurate than the spectral line intensity method.

Real-Time Monitoring of SO2 Emissions Using a UV Camera with Built-in NO2 and Aerosol Corrections.

Nitrogen dioxide (NO2) absorption correction of the sulfur dioxide (SO2) camera was demonstrated for the first time. The key to improving the measurement accuracy is to combine a differential optical absorption spectroscopy (DOAS) instrument with the SO2 camera for the real-time NO2 absorption correction and aerosol scattering correction. This method performs NO2 absorption correction by the correlation between the NO2 column density measurement of the DOAS and the NO2 optical depth of the corresponding channel from the SO2 camera at a narrow wavelength window around 310 and 310 nm. The error of correction method is estimated through comparison with only using the second channel of the traditional SO2 camera to correct for aerosol scattering and it can be reduced by 11.3% after NO2 absorption corrections. We validate the correction method through experiments and demonstrate it to be of greatly improved accuracy. The result shows that the ultraviolet (UV) SO2 camera system with NO2 absorption corrections appears to have great application prospects as a technology for visualized real-time monitoring of SO2 emissions.

Development of a self-calibration method for real-time monitoring of SO2 ship emissions with UV cameras.

Self-calibration of UV cameras was demonstrated for the first time. This novel method has the capability of real-time continuous calibration by using the raw images at 310 nm and 330 nm without changing the viewing direction or adding any additional equipment. The methodology was verified through simulations and experiments and demonstrated to be of greatly improved effectiveness and accuracy. The errors of self-calibration mothed are estimated by comparison with the differential optical absorption spectroscopy (DOAS) approach, and it can be reduced to 1.8% after filter transmittance corrections. The results show that the self-calibration method appears to have great potential as a future technique for quantitative and visual real-time monitoring of SO2 emissions from ships and other point sources (such as oil refineries, power plants, or more broadly, any industrial stack) when the field of view (FOV) of the system is not completely covered by the SO2 plumes.

Real-time continuous calibration method for an ultraviolet camera.

The accuracy of SO2 cameras is significantly determined by the ability to obtain an accurate calibration. This work presents a real-time continuous calibration method for SO2 cameras with a moderate resolution spectrometer by taking realistic radiative transfer into account. The effectiveness and accuracy of the proposed method have been verified through simulations and experiments. The calibration error can be reduced by about 20-80% compared with the commonly used cell calibration, especially for situations of long distance, poor visibility, or optically thick plumes.

Simulation and application of the emission line O19P18 of O2(a1Δg) dayglow near 1.27 µm for wind observations from limb-viewing satellites.

The O2(a1Δg) emission near 1.27 µm has relatively bright signal and extended altitude coverage and provides an important means to remotely sense the compositional structures and dynamical features of the upper atmosphere globally. In this paper, we report the simulation and application of O2(a1Δg) dayglow near 1.27 µm for wind observations from limb-viewing satellites. A line by line radiative transfer model of the O2(aΔ1g,υ'=0)→O2(XΣ3g,υ″=0) band is developed by taking both multiple scattering radiative transfer and nonlocal thermal equilibrium (non-LTE) models into account. The emission line O19P18 (7772.030 cm-1) with weak self-absorption, bright radiation intensity, and large spectral separation range is proved to be suitable for limb-viewing wind detection, due to its advantages of significantly lower cost, risk, and platform requirements. In order to ascertain the wind precision of O19P18, observations by a DASH-type (the Doppler asymmetric spatial heterodyne) instrument are simulated. The simulated results indicate a wind measurement precision of 1-2 m/s over an altitude range of 40 to 70 km in general, and possibly to 2-4 m/s due to a strong dependence on the spectral interference of the scattered sunlight background.

Development of an imaging gas correlation spectrometry based mid-infrared camera for two-dimensional mapping of CO in vehicle exhausts.

Real-time imaging of CO in vehicle exhaust was demonstrated using a gas correlation spectrometry based mid-infrared camera for the first time. The novel gas-correlation imaging technique is used to eliminate the spectral interferences from background radiation and other major combustion products, and reduce the influences of the optical jitter and temperature variations, thereby identifying and quantifying the gas. We take several spectral factors into account for the instrument design, concentration calibration and data evaluation, including atmospheric transmission, radiation interference, as well as the spectral response of infrared camera, filter and gas cell. A calibration method based on the molecular spectroscopy and radiative transfer equation is developed to identify the numerical relationship between the CO concentration × length and the measured image intensity. Two-dimensional CO distribution of vehicle exhaust with a time resolution of 50 Hz and detection limit of 20 ppm × meter is achieved when the distance between optical equipment and engine nozzle is 3 m. The gas correlation spectrometry based mid-infrared camera shows a great potential as a future technique to monitor vehicle pollution emissions quantitatively and visually.

2D visualization of hot gas based on a mid-infrared molecular Faraday imaging filter.

This Letter presents recent results on, to the best of our knowledge, the first experimental demonstration of a mid-infrared molecular Faraday imaging filter (MOFIF)-based camera for hot gas visualization. Gas-phase nitric oxide (NO) is used as the working material of the MOFIF due to the fact that NO is the typical representative of the paramagnetic species and plays an important role in the chemical and physical process of combustion reaction. The MOFIF transmission with comb-like transmittance spectrum is elaborately designed and matches well with the radiation spectrum of NO gas. Pure NO infrared images have been well captured in a combustion environment, and shown as a video that demonstrates the imaging capability and gas selectivity of MOFIF.

Demonstration of a mid-infrared NO molecular Faraday optical filter.

A molecular Faraday optical filter (MFOF) working in the mid-infrared region is realized for the first time. NO molecule was used as the working material of the MFOF for potential applications in atmospheric remote sensing and combustion diagnosis. We develop a complete theory to describe the performance of MFOF by taking both Zeeman absorption and Faraday rotation into account. We also record the Faraday rotation transmission (FRT) signal using a quantum cascade laser over the range of 1,820 cm-1 to 1,922 cm-1 and calibrate it by using a 101.6 mm long solid germanium etalon with a free spectral range of 0.012 cm-1. Good agreement between the simulation results and experimental data is achieved. The NO-MFOF's transmission characteristics as a function of magnetic field and pressure are studied in detail. Both Comb-like FRT spectrum and single branch transmission spectrum are obtained by changing the magnetic field. The diversity of FRT spectrum expands the range of potential applications in infrared optical remote sensing. This filtering method can also be extended to the lines of other paramagnetic molecules.

Self-Regulation Phenomenon Emerged During Prolonged Fatigue Driving: An EEG Connectivity Study.

Driving fatigue is a common experience for most drivers and can reduce human cognition and judgment abilities. Previous studies have exhibited a phenomenon of the non-monotonically varying indicators (both behavioral and neurophysiological) for driving fatigue evaluation but paid little attention to this phenomenon. Herein, we propose a hypothesis that the non-monotonically varying phenomenon is caused by the self-regulation of brain activity, which is defined as the fatigue self-regulation (FSR) phenomenon. In this study, a 90-min simulated driving task was performed on 26 healthy university students. EEG data and reaction time (RT) were synchronously recorded during the whole task. To identify the FSR phenomenon, a data-driven criterion was proposed based on clustering analysis of individual behavioral data and the FSR group was determined as having non-monotonic increase trend of RT and the drops of RT during prolonged driving were more than two levels among the total five levels. The subjects were then divided into two groups: the FSR group and the non-FSR group. Quantitative comparative analysis showed significant differences in behavioral performance, functional connectivity, network characteristics, and classification performance between the FSR and non-FSR groups. Specifically, the behavioral performance exhibited apparent non-monotonic development trend: increasing-decreasing-increasing. Moreover, network characteristics presented similar self-regulated development trends. Our study provides a new insight for revealing the complex neural mechanisms of driving fatigue, which may promote the development of practical techniques for automatic detection method and mitigation strategy.

Individualized Prediction of Task Performance Decline Using Pre-Task Resting-State Functional Connectivity.

As a common complaint in contemporary society, mental fatigue is a key element in the deterioration of the daily activities known as time-on-task (TOT) effect, making the prediction of fatigue-related performance decline exceedingly important. However, conventional group-level brain-behavioral correlation analysis has the limitation of generalizability to unseen individuals and fatigue prediction at individual-level is challenging due to the significant differences between individuals both in task performance efficiency and brain activities. Here, we introduced a cross-validated data-driven analysis framework to explore, for the first time, the feasibility of utilizing pre-task idiosyncratic resting-state functional connectivity (FC) on the prediction of fatigue-related task performance degradation at individual level. Specifically, two behavioral metrics, namely ∆RT (between the most vigilant and fatigued states) and TOTslope over the course of the 15-min sustained attention task, were estimated among three sessions from 37 healthy subjects to represent fatigue-related individual behavioral impairment. Then, a connectome-based prediction model was employed on pre-task resting-state FC features, identifying the network-related differences that contributed to the prediction of performance deterioration. As expected, prominent populational TOT-related performance declines were revealed across three sessions accompanied with substantial inter-individual differences. More importantly, we achieved significantly high accuracies for individualized prediction of both TOT-related behavioral impairment metrics using pre-task neuroimaging features. Despite the distinct patterns between both behavioral metrics, the identified top FC features contributing to the individualized predictions were mainly resided within/between frontal, temporal and parietal areas. Overall, our results of individualized prediction framework extended conventional correlation/classification analysis and may represent a promising avenue for the development of applicable techniques that allow precaution of the TOT-related performance declines in real-world scenarios.