A comparative study of stereo-dependent SSVEP targets and their impact on VR-BCI performance.

Steady-state visual evoked potential brain-computer interfaces (SSVEP-BCI) have attracted significant attention due to their ease of deployment and high performance in terms of information transfer rate (ITR) and accuracy, making them a promising candidate for integration with consumer electronics devices. However, as SSVEP characteristics are directly associated with visual stimulus attributes, the influence of stereoscopic vision on SSVEP as a critical visual attribute has yet to be fully explored. Meanwhile, the promising combination of virtual reality (VR) devices and BCI applications is hampered by the significant disparity between VR environments and traditional 2D displays. This is not only due to the fact that screen-based SSVEP generally operates under static, stable conditions with simple and unvaried visual stimuli but also because conventional luminance-modulated stimuli can quickly induce visual fatigue. This study attempts to address these research gaps by designing SSVEP paradigms with stereo-related attributes and conducting a comparative analysis with the traditional 2D planar paradigm under the same VR environment. This study proposed two new paradigms: the 3D paradigm and the 3D-Blink paradigm. The 3D paradigm induces SSVEP by modulating the luminance of spherical targets, while the 3D-Blink paradigm employs modulation of the spheres' opacity instead. The results of offline 4-object selection experiments showed that the accuracy of 3D and 2D paradigm was 85.67 and 86.17% with canonical correlation analysis (CCA) and 86.17 and 91.73% with filter bank canonical correlation analysis (FBCCA), which is consistent with the reduction in the signal-to-noise ratio (SNR) of SSVEP harmonics for the 3D paradigm observed in the frequency-domain analysis. The 3D-Blink paradigm achieved 75.00% of detection accuracy and 27.02 bits/min of ITR with 0.8 seconds of stimulus time and task-related component analysis (TRCA) algorithm, demonstrating its effectiveness. These findings demonstrate that the 3D and 3D-Blink paradigms supported by VR can achieve improved user comfort and satisfactory performance, while further algorithmic optimization and feature analysis are required for the stereo-related paradigms. In conclusion, this study contributes to a deeper understanding of the impact of binocular stereoscopic vision mechanisms on SSVEP paradigms and promotes the application of SSVEP-BCI in diverse VR environments.

Average Time Consumption Per Character - a Practical Performance Metric for Generic Synchronous BCI Spellers.

OBJECTIVE: The information transfer rate (ITR) is widely accepted as a performance metric for generic brain-computer interface (BCI) spellers, while it is noticeable that the communication speed given by ITR is actually an upper bound which however can never be reached in real systems. A new performance metric is therefore needed. METHODS: In this paper, a new metric named average time consumption per character (ATCPC) is proposed. It quantifies how long it takes on average to type one character using a typical synchronous BCI speller. To analytically derive ATCPC, the real typing process is modelled with a random walk on a graph. Misclassification and backspace are carefully characterized. A close-form formula of ATCPC is obtained through computing the hitting time of the random walk. The new metric is validated through simulated typing experiments and compared with ITR. RESULTS: Firstly, the formula and simulation show a good consistency. Secondly, ITR always tends to overestimate the communication speed, while ATCPC is more realistic. CONCLUSION: The proposed ATCPC metric is valid. SIGNIFICANCE: ATCPC is a qualified substitute for ITR. ATCPC also reveals the great potential of keyboard optimization to further enhance the performance of BCI spellers, which was hardly investigated before.

A Subject-Specific Attention Index Based on the Weighted Spectral Power.

As an essential cognitive function, attention has been widely studied and various indices based on EEG have been proposed for its convenience and easy availability for real-time attention monitoring. Although existing indices based on spectral power of empirical frequency bands are able to describe the attentional state in some way, the reliability still needs to be improved. This paper proposed a subject-specific attention index based on the weighted spectral power. Unlike traditional indices, the ranges of frequency bands are not empirical but obtained from subject-specific change patterns of spectral power of electroencephalograph (EEG) to overcome the great inter-subject variance. In addition, the contribution of each frequency component in the frequency band is considered different. Specifically, the ratio of power spectral density (PSD) function in attentional and inattentional state is utilized to calculate the weight to enhance the effectiveness of the proposed index. The proposed subject-specific attention index based on the weighted spectral power is evaluated on two open datasets including EEG data of a total of 44 subjects. The results of the proposed index are compared with 3 traditional attention indices using various statistical analysis methods including significance tests and distribution variance measurements. According to the experimental results, the proposed index can describe the attentional state more accurately. The proposed index respectively achieves accuracies of 86.21% and 70.00% at the 1% significance level in both the t-test and Wilcoxon rank-sum test for two datasets, which obtains improvements of 41.38% and 20.00% compared to the best result of the traditional indices. These results indicate that the proposed index provides an efficient way to measure attentional state.

Attentional State Classification Using Amplitude and Phase Feature Extraction Method Based on Filter Bank and Riemannian Manifold.

As a significant aspect of cognition, attention has been extensively studied and numerous measurements have been developed based on brain signal processing. Although existing attentional state classification methods have achieved good accuracy by extracting a variety of handcrafted features, spatial features have not been fully explored. This paper proposes an attentional state classification method based on Riemannian manifold to utilize spatial information. Based on the concept of Riemannian manifold of symmetric positive definite (SPD) matrix, the proposed method exploits the structure of covariance matrix to extract spatial features instead of using spatial filters. Specifically, Riemannian distances from intra-class Riemannian means are extracted as features for their robustness. To fully extend the potential of electroencephalograph (EEG) signal, both amplitude and phase information is utilized. In addition, to solve the variance of frequency bands, a filter bank is employed to process the signal of different frequency bands separately. Finally, features are fed into a support vector machine with a polynomial kernel to obtain classification results. The proposed attentional state classification using amplitude and phase feature extraction method based on filter bank and Riemannian manifold (AP-FBRM) method is evaluated on two open datasets including EEG data of 29 and 26 subjects. According to the experimental results, the optimal set of filter bank and the optimal technique to extract features containing both amplitude and phase information are determined. The proposed method respectively achieves accuracies of 88.06% and 80.00% and outperforms 8 baseline methods, which manifests that the proposed method creates an efficient way to recognize attentional state.

A Novel Hybrid Brain-Computer Interface Combining the Illusion-Induced VEP and SSVEP.

Traditional single-modality brain-computer interface (BCI) systems are limited by their reliance on a single characteristic of brain signals. To address this issue, incorporating multiple features from EEG signals can provide robust information to enhance BCI performance. In this study, we designed and implemented a novel hybrid paradigm that combined illusion-induced visual evoked potential (IVEP) and steady-state visual evoked potential (SSVEP) with the aim of leveraging their features simultaneously to improve system efficiency. The proposed paradigm was validated through two experimental studies, which encompassed feature analysis of IVEP with a static paradigm, and performance evaluation of hybrid paradigm in comparison with the conventional SSVEP paradigm. The characteristic analysis yielded significant differences in response waveforms among different motion illusions. The performance evaluation of the hybrid BCI demonstrates the advantage of integrating illusory stimuli into the SSVEP paradigm. This integration effectively enhanced the spatio-temporal features of EEG signals, resulting in higher classification accuracy and information transfer rate (ITR) within a short time window when compared to traditional SSVEP-BCI in four-command task. Furthermore, the questionnaire results of subjective estimation revealed that proposed hybrid BCI offers less eye fatigue, and potentially higher levels of concentration, physical condition, and mental condition for users. This work first introduced the IVEP signals in hybrid BCI system that could enhance performance efficiently, which is promising to fulfill the requirements for efficiency in practical BCI control systems.

Joint User Association and Deployment Optimization for Energy-Efficient Heterogeneous UAV-Enabled MEC Networks.

Unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services have become a promising technology. However, the limited energy supply of UAVs, which constrains their service duration, has emerged as an obstacle in UAV-enabled networks. In this context, a novel task offloading framework is proposed in UAV-enabled mobile edge computing (MEC) networks. Specifically, heterogeneous UAVs with different communication and computing capabilities are considered and the energy consumption of UAVs is minimized via jointly optimizing user association and UAV deployment. The optimal transport theory is introduced to analyze the user association sub-problem, and the UAV deployment for each sub-region is determined by a dragonfly algorithm (DA). Simulation results show that the energy consumption performance is significantly improved by the proposed algorithm.

A Sliced Parabolic Equation Method to Characterize Maritime Radio Propagation.

For maritime broadband communications, atmospheric ducts can enable beyond line-of-sight communications or cause severe interference. Due to the strong spatial-temporal variability of atmospheric conditions in near-shore areas, atmospheric ducts have inherent spatial heterogeneity and suddenness. This paper aims to evaluate the effect of horizontally inhomogeneous ducts on maritime radio propagation through theoretical analysis and measurement validation. To make better use of meteorological reanalysis data, we design a range-dependent atmospheric duct model. Then, a sliced parabolic equation algorithm is proposed to improve the prediction accuracy of path loss. We derive the corresponding numerical solution and analyze the feasibility of the proposed algorithm under the range-dependent duct conditions. A 3.5 GHz long-distance radio propagation measurement is utilized to verify the algorithm. The spatial distribution characteristics of atmospheric ducts in the measurements are analyzed. Based on actual duct conditions, the simulation results are consistent with the measured path loss. The proposed algorithm outperforms the existing method during the multiple duct periods. We further investigate the influence of different duct horizontal characteristics on the received signal strength.

MDTL: A Novel and Model-Agnostic Transfer Learning Strategy for Cross-Subject Motor Imagery BCI.

In recent years, deep neural network-based transfer learning (TL) has shown outstanding performance in EEG-based motor imagery (MI) brain-computer interface (BCI). However, due to the long preparation for pre-trained models and the arbitrariness of source domain selection, using deep transfer learning on different datasets and models is still challenging. In this paper, we proposed a multi-direction transfer learning (MDTL) strategy for cross-subject MI EEG-based BCI. This strategy utilizes data from multi-source domains to the target domain as well as from one multi-source domain to another multi-source domain. This strategy is model-independent so that it can be quickly deployed on existing models. Three generic deep learning models for MI classification (DeepConvNet, ShallowConvNet, and EEGNet) and two public motor imagery datasets (BCIC IV dataset 2a and Lee2019) are used in this study to verify the proposed strategy. For the four-classes dataset BCIC IV dataset 2a, the proposed MDTL achieves 80.86%, 81.95%, and 75.00% mean prediction accuracy using the three models, which outperforms those without MDTL by 5.79%, 6.64%, and 11.42%. For the binary-classes dataset Lee2019, MDTL achieves 88.2% mean accuracy using the model DeepConvNet. It outperforms the accuracy without MDTL by 23.48%. The achieved 81.95% and 88.2% are also better than the existing deep transfer learning strategy. Besides, the training time of MDTL is reduced by 93.94%. MDTL is an easy-to-deploy, scalable and reliable transfer learning strategy for existing deep learning models, which significantly improves model performance and reduces preparation time without changing model architecture.

Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks.

The Internet-of-Things (IoT) massive access is a significant scenario for sixth-generation (6G) communications. However, low-power IoT devices easily suffer from remote interference caused by the atmospheric duct under the 6G time-division duplex (TDD) mode. It causes distant downlink wireless signals to propagate beyond the designed protection distance and interfere with local uplink signals, leading to a large outage probability. In this paper, a remote interference discrimination testbed is originally proposed to detect interference, which supports the comparison of different types of algorithms on the testbed. Specifically, 5,520,000 TDD network-side data collected by real sensors are used to validate the interference discrimination capabilities of nine promising AI algorithms. Moreover, a consistent comparison of the testbed shows that the ensemble algorithm achieves an average accuracy of 12% higher than the single model algorithm.

Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis.

BACKGROUND: The brain-computer interface (BCI) is a highly cross-discipline technology and its successful application in various domains has received increasing attention. However, the BCI-enabled automobile industry is has been comparatively less investigated. In particular, there are currently no studies focusing on brain-controlled driving mode selection. Specifically, different driving modes indicate different driving styles which can be selected according to the road condition or the preference of individual drivers. METHODS: In this paper, a steady-state visual-evoked potential (SSVEP)-based driving mode selection system is proposed. Upon this system, drivers can select the intended driving modes by only gazing at the corresponding SSVEP stimuli. A novel EEG processing algorithm named inter-trial distance minimization analysis (ITDMA) is proposed to enhance SSVEP detection. Both offline and real-time experiments were carried out to validate the effectiveness of the proposed system. CONCLUSION: The results show that a high selection accuracy of up to 92.3% can be realized, although this depends on the specific choice of flickering duration, the number of EEG channels, and the number of training signals. Additionally, energy consumption is investigated in terms of which the proposed brain-controlled system considerably differs from a traditional driving mode selection system, and the main reason is shown to be the existence of a detection error.

Prevalence of Azole-Resistant Aspergillus fumigatus is Highly Associated with Azole Fungicide Residues in the Fields.

Triazole resistance in Aspergillus fumigatus is a growing public health concern. In addition to its emergence in the therapy of invasive aspergillosis by triazole medicines, it has been frequently detected in agricultural fields all over the world. Here, we explore the potential link between residues of azole fungicides with similar chemical structure to triazole medicines in soil and the emergence of resistant A. fumigatus (RAF) through 855 500 km2 monitoring survey in Eastern China covering 6 provinces. In total, 67.3%, 15.2%, 12.3%, 2.9%, 1.5%, 0.4%, and 0.3% of the soil samples contained these five fungicides (tebuconazole, difenoconazole, propiconazole, hexaconazole, and prochloraz) of 0-100, 100-200, 200-400, 400-600, 600-800, 800-1000, and >1000 ng/g, respectively. The fractions of samples containing RAF isolates were 2.4%, 5.2%, 6.4%, 7.7%, 7.4%, 14.3%, and 20.0% of the samples with total azole fungicide residues of 0-100, 100-200, 200-400, 400-600, 600-800, 800-1000, and >1000 ng/g, respectively. We find that the prevalence of RAFs is positively (P < 0.0001) correlated with residual levels of azole fungicides in soils. Our results suggest that the use of azole fungicides in agriculture should be minimized and the intervals between treatments expanded to reduce the selective pressure toward the development of resistance in A. fumigatus in agricultural fields.

Dynamic modeling of famoxadone and oxathiapiprolin residue on cucumber and Chinese cabbage based on tomato and lettuce archetypes.

We analyzed the uptake and distribution of two pesticides (famoxadone and oxathiapiprolin) in herbaceous vegetables (cucumber and tomato) and leafy vegetables (Chinese cabbage and lettuce) to test the viability of applying existing archetypes in the dynamic plant uptake model dynamiCROP to modeling pesticide residue in other crops. Using field data and modeling, we showed that tomato was an unsuitable match for cucumber (R2 of 0.5325-0.6862) though lettuce was a good fit for Chinese cabbage (R2 of 0.8649-0.8862). We then used our cucumber data to add this as a new crop species archetype in dynamiCROP; further tests proved the accuracy of this approach (R2 of 0.8097-0.9152). In addition, we analyzed the distribution, uptake, and translocation of the two pesticides in cucumber and Chinese cabbage, using the model to better understand the mechanisms of pesticide residues over time and evaluate potential human exposure to pesticide residues from consumption of these crops. The fractions of famoxadone and oxathiapiprolin eventually ingested by humans based on our field trials ranged from 10-4 to 10-3 kg intake kg applied-1; that is, per kilogram of pesticide applied, humans would eventually consume less than one gram.

Determination and distribution of pesticides and antibiotics in agricultural soils from northern China.

Different types of soil samples from a typical farmland in northern China were collected and evaluated for the presence of the pesticides and antibiotics. 47 pesticides were extracted with a quick, easy, cheap, effective, rugged, and safe (QuEChERS) preparation method and cleanup with 50 mg C18, while 10 antibiotics were extracted with methanol/EDTA-McIlvaine buffer solution (v/v = 1/1), then both of them were analyzed with high performance liquid chromatography-tandem mass spectrometer (HPLC-MS/MS). Total concentrations of the 47 pesticides in the soil samples ranged from not detectable (ND) to 3.8 mg kg-1. The soil exhibited relatively high ecological risk for atrazine, chlorpyrifos, tebuconazole, difenoconazole, pymetrozine, and thiamethoxam, as over 1.0% of the sample concentrations exceeded 0.1 mg kg-1. The residual levels of the 10 antibiotics were relatively low (ND-951.0 µg kg-1). Tetracyclines exhibited a high detection rate (20.9%), with 2.8% of the soil samples exhibiting tetracyclines concentrations exceeding 100 µg kg-1, implying high ecological risk. The 4 sulfonamides and 2 macrolides analyzed showed detection rates below 0.8%. Spatial changes in the distribution of pesticides and antibiotics appear to be related to land use patterns, particularly orchards and vegetable plots. The over-standard rate of pesticides and antibiotics in orchards was greater than that of vegetable plots, and grain fields had the lowest over-standard rate. These data were helpful to figure out the pollution of these pesticides and antibiotics, and provided valuable information for soil quality assessment and risk assessment.

Measured and Modeled Residue Dynamics of Famoxadone and Oxathiapiprolin in Tomato Fields.

A reliable analytical method for the simultaneous determination of famoxadone and oxathiapiprolin dissipation kinetics as well as the metabolites of oxathiapiprolin (IN-E8S72 and IN-WR791) in tomato and soil was developed. We studied the dissipation of famoxadone and oxathiapiprolin in tomatoes grown using different kinetic curves in the area of Beijing in 2015 and 2016. Our results show that the most suitable model for two fungicides in 2015 and 2016 was first-order kinetic and second-order kinetic with the half-lives of 3.4 to 5.2 and 2.4 to 3.0 days, respectively. In addition, we applied the dynamic plant uptake model dynamiCROP and combined it with results from the field experiments to investigate the uptake and translocation of famoxadone and oxathiapiprolin in the soil-tomato environment. Modeled and measured results of two years fitted well with R2 values ranging from 0.8072 to 0.9221. The fractions of famoxadone and oxathiapiprolin applied during tomato cultivation that are eventually ingested by humans via residues in crop harvest were finally evaluated and found to be in the range of one part per thousand, that is one gram intake per kilogram applied.

Efficient synthesis of a 6-deoxy-talose containing tetrasccharide found in Franconibacter helveticus LMG23732T.

Synthesis of the 6-deoxy-talose (6-dTal) containing tetrasaccharide, naturally found in Franconibacter helveticus LMG23732T, has been described. The synthetic method utilized an allyloxyethylidene group for protecting the 1-OH and 2-OH groups of rhamnopyranose and a redox reaction for synthesizing 6-deoxy talose, which eventually formed a disaccharide containing α-Glcp-(1→2)-6dTalp configured glycosidic bonds using a [2 + 2] synthetic strategy.

Residue and risk assessment of fluopicolide and cyazofamid in grapes and soil using LC-MS/MS and modified QuEChERS.

The residue behavior of fluopicolide, cyazofamid and their metabolites (M-01, M-02 and CCIM) was evaluated in open field conditions. The dissipation and terminal residue of these five compounds were determined via a modified QuEChERS method, by adjusting the liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) conditions and optimizing the purification process. This led to a satisfactory average recovery of between 71.6% and 107.7%, as well as limit of quantitation (LOQ) values of 0.05 mg kg-1. The dissipation results recorded in two places in China illustrated that the half-life values of fluopicolide are 11.4 (Anhui, grape), 19.7 (Anhui, soil) and 21.8 (Hebei, grape), 21.2 (Hebei, soil) days, respectively. As for the dissipation of cyazofamid, it was found to have half-life values of 8.7 (Anhui, grape) and 20.1 (Hebei, grape) days. The final residues in grapes were found to be below the maximum residue limit (MRL) of 2 mg kg-1 for fluopicolide and 1 mg kg-1 for cyazofamid. Thus, a preharvest interval of 10 days and recommended MRLs from the Joint FAO/WHO Meeting on Pesticide Residues (JMPR) are appropriate to ensure the food safety of fluopicolide and cyazofamid in grapes. The hazard quotient (HQ) and acute hazard index (aHI) values were found to be below 100%, demonstrating negligible risk in consuming grapes, regardless of long or short-term exposure.

Characterization and genome functional analysis of the DDT-degrading bacterium Ochrobactrum sp. DDT-2.

A strain of Ochrobactrum sp. DDT-2 that was capable of degrading DDT as the sole carbon and energy source was isolated and sequenced, and its biodegradation characteristics and metabolism mechanism were examined. The genome sequence of the isolate DDT-2 was composed of 4,630,303bp with a GC content of 55.99% and 4454 coding genes. The degradation rate of DDT by the isolate DDT-2 increased with the increasing substrate concentration (0.1-10mg/l) and temperature (20-40°C). The degradation half-life of DDT in the presence of the isolate DDT-2 at pH7.0 was obviously shorter than those at pH5.0 and 9.0. Potential DDT degradation genes were found in the isolate DDT-2 genome by a BLASTx search against a DDT degradation genes (DDGs) database. A common biodegradation pathway of DDT was proposed based on the combined analysis of genome annotation and mass spectrometry. DDT was initially dechlorinated to form DDD and DDE. Then, it was transformed into DDMU and DDA via dechlorination and carboxylation, and it may ultimately be mineralized to carbon dioxide. The results suggested that the isolate DDT-2 could be useful for the bioremediation of DDT and its metabolite residues.

Characterization and genome functional analysis of a novel metamitron-degrading strain Rhodococcus sp. MET via both triazinone and phenyl rings cleavage.

A novel bacterium capable of utilizing metamitron as the sole source of carbon and energy was isolated from contaminated soil and identified as Rhodococcus sp. MET based on its morphological characteristics, BIOLOG GP2 microplate profile, and 16S rDNA phylogeny. Genome sequencing and functional annotation of the isolate MET showed a 6,340,880 bp genome with a 62.47% GC content and 5,987 protein-coding genes. In total, 5,907 genes were annotated with the COG, GO, KEGG, Pfam, Swiss-Prot, TrEMBL, and nr databases. The degradation rate of metamitron by the isolate MET obviously increased with increasing substrate concentrations from 1 to 10 mg/l and subsequently decreased at 100 mg/l. The optimal pH and temperature for metamitron biodegradation were 7.0 and 20-30 °C, respectively. Based on genome annotation of the metamitron degradation genes and the metabolites detected by HPLC-MS/MS, the following metamitron biodegradation pathways were proposed: 1) Metamitron was transformed into 2-(3-hydrazinyl-2-ethyl)-hydrazono-2-phenylacetic acid by triazinone ring cleavage and further mineralization; 2) Metamitron was converted into 3-methyl-4-amino-6(2-hydroxy-muconic acid)-1,2,4-triazine-5(4H)-one by phenyl ring cleavage and further mineralization. The coexistence of diverse mineralization pathways indicates that our isolate may effectively bioremediate triazinone herbicide-contaminated soils.