Novel annual nitrogen management strategy improves crop yield and reduces greenhouse gas emissions in wheat-maize rotation systems under limited irrigation.

Excessive irrigation and nitrogen application have long seriously undermined agricultural sustainability in the North China Plain (NCP), leading to declining groundwater tables and intensified greenhouse gas (GHG) emissions. Developing low-input management practices that meet the growing food demand while reducing environmental costs is urgently needed. Here, we developed a novel nitrogen management strategy for a typical winter wheat-summer maize rotation system in the NCP under limited irrigation (wheat sowing irrigation only (W0) or sowing and jointing irrigation (W1)) and low nitrogen input (360 kg N ha-1, about 70 % of traditional annual nitrogen input). Novel nitrogen management strategy promoted efficient nitrogen fertilizer uptake and utilization by both crops via optimization of nitrogen fertilizer allocation between the two crops, i.e., increasing nitrogen inputs to wheat (from 180 to 240 kg N ha-1) while reducing nitrogen inputs to maize (from 180 to 120 kg N ha-1). Three-year field study demonstrated that integrated management practices combining novel nitrogen management strategy with limited irrigation increased annual yields and PFPN by 1.9-5.7 %, and reduced TGE by 55-68 kg CO2-eq ha-1 and GHGI by 2.2-10.3 %, without any additional cost. Our results provide agricultural operators and policymakers with practical and easy-to-scalable integrated management strategy, and offer key initiative to promote grain production in the NCP towards agriculture sustainable intensification with high productivity and efficiency, water conservation and emission reduction.

Association between host nitrogen absorption and root-associated microbial community in field-grown wheat.

Plant roots and rhizosphere soils assemble diverse microbial communities, and these root-associated microbiomes profoundly influence host development. Modern wheat has given rise to numerous cultivars for its wide range of ecological adaptations and commercial uses. Variations in nitrogen uptake by different wheat cultivars are widely observed in production practices. However, little is known about the composition and structure of the root-associated microbiota in different wheat cultivars, and it is not sure whether root-associated microbial communities are relevant in host nitrogen absorption. Therefore, there is an urgent need for systematic assessment of root-associated microbial communities and their association with host nitrogen absorption in field-grown wheat. Here, we investigated the root-associated microbial community composition, structure, and keystone taxa in wheat cultivars with different nitrogen absorption characteristics at different stages and their relationships with edaphic variables and host nitrogen uptake. Our results indicated that cultivar nitrogen absorption characteristics strongly interacted with bacterial and archaeal communities in the roots and edaphic physicochemical factors. The impact of host cultivar identity, developmental stage, and spatial niche on bacterial and archaeal community structure and network complexity increased progressively from rhizosphere soils to roots. The root microbial community had a significant direct effect on plant nitrogen absorption, while plant nitrogen absorption and soil temperature also significantly influenced root microbial community structure. The cultivar with higher nitrogen absorption at the jointing stage tended to cooperate with root microbial community to facilitate their own nitrogen absorption. Our work provides important information for further wheat microbiome manipulation to influence host nitrogen absorption. KEY POINTS: • Wheat cultivar and developmental stage affected microbiome structure and network. • The root microbial community strongly interacted with plant nitrogen absorption. • High nitrogen absorption cultivar tended to cooperate with root microbiome.

Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series.

As a novel form of visual analysis technique, the Poincaré plot has been used to identify correlation patterns in time series that cannot be detected using traditional analysis methods. In this work, based on the nonextensive of EEG, Poincaré plot nonextensive distribution entropy (NDE) is proposed to solve the problem of insufficient discrimination ability of Poincaré plot distribution entropy (DE) in analyzing fractional Brownian motion time series with different Hurst indices. More specifically, firstly, the reasons for the failure of Poincaré plot DE in the analysis of fractional Brownian motion are analyzed; secondly, in view of the nonextensive of EEG, a nonextensive parameter, the distance between sector ring subintervals from the original point, is introduced to highlight the different roles of each sector ring subinterval in the system. To demonstrate the usefulness of this method, the simulated time series of the fractional Brownian motion with different Hurst indices were analyzed using Poincaré plot NDE, and the process of determining the relevant parameters was further explained. Furthermore, the published sleep EEG dataset was analyzed, and the results showed that the Poincaré plot NDE can effectively reflect different sleep stages. The obtained results for the two classes of time series demonstrate that the Poincaré plot NDE provides a prospective tool for single-channel EEG time series analysis.

Effects of Paradigm Color and Screen Brightness on Visual Fatigue in Light Environment of Night Based on Eye Tracker and EEG Acquisition Equipment.

Nowadays, more people tend to go to bed late and spend their sleep time with various electronic devices. At the same time, the BCI (brain−computer interface) rehabilitation equipment uses a visual display, thus it is necessary to evaluate the problem of visual fatigue to avoid the impact on the training effect. Therefore, it is very important to understand the impact of using electronic devices in a dark environment at night on human visual fatigue. This paper uses Matlab to write different color paradigm stimulations, uses a 4K display with an adjustable screen brightness to jointly design the experiment, uses eye tracker and g.tec Electroencephalogram (EEG) equipment to collect the signal, and then carries out data processing and analysis, finally obtaining the influence of the combination of different colors and different screen brightness on human visual fatigue in a dark environment. In this study, subjects were asked to evaluate their subjective (Likert scale) perception, and objective signals (pupil diameter, θ + α frequency band data) were collected in a dark environment (<3 lx). The Likert scale showed that a low screen brightness in the dark environment could reduce the visual fatigue of the subjects, and participants preferred blue to red. The pupil data revealed that visual perception sensitivity was more vulnerable to stimulation at a medium and high screen brightness, which is easier to deepen visual fatigue. EEG frequency band data concluded that there was no significant difference between paradigm colors and screen brightness on visual fatigue. On this basis, this paper puts forward a new index­the visual anti-fatigue index, which provides a valuable reference for the optimization of the indoor living environment, the improvement of satisfaction with the use of electronic equipment and BCI rehabilitation equipment, and the protection of human eyes.

Quantitative and objective diagnosis of color vision deficiencies based on steady-state visual evoked potentials.

PURPOSE: Traditional color vision tests depend on subjective judgments and are not suitable for infant children and subjects with cognitive dysfunction. We aimed to explore an objective and quantitative color vision testing method based on sweep steady-state visual evoked potentials (sweep SSVEPs) and compare the results with subjective Farnsworth-Munsell (FM) 100-hue test results. METHODS: A red-green SSVEP pattern reversal checkboard paradigm at different luminance ratios was used to induce visual evoked potentials (VEPs) from 15 color vision deficiencies (CVDs) and 11 normal color vision subjects. After electroencephalography signals were processed by canonical correlation analysis, an equiluminance turning curve corresponding to the activation of the L-cones and M-cones at different levels of color vision was established. Then, we obtained different equiluminance T and proposed the SSVEP color vision severity index (ICVD) to quantify color vision function and the severity of CVDs. In addition, the FM 100-hue test was used to obtain subjective data for the diagnosis of color vision. RESULTS: The value of ICVD can be an indicator of the level of color vision. Both the total error scores (TES) and confusion index (C-index) of the FM 100-hue test were significantly correlated with ICVD values (P < 0.001, respectively). ICVD also had a good classification effect in detecting normals, anomalous trichromats and dichromats. Moreover, equiluminance T had a good effect on classifying protans and deutans in subjects with CVDs. CONCLUSION: Color vision evaluation with sweep SSVEPs showed a good correlation with subjective psychophysical methods. SSVEPs can be an objective and quantitative method to test color vision and diagnose CVDs.

Comparison of the performance of six stimulus paradigms in visual acuity assessment based on steady-state visual evoked potentials.

PURPOSE: There are several stimulus paradigms used in objective visual acuity assessment based on steady-state visual evoked potentials (SSVEPs). The aim of this study was to explore the difference and performance of common used six stimulus paradigms (reverse vertical sinusoidal gratings, reverse horizontal sinusoidal gratings, reverse vertical square-wave gratings, brief-onset vertical sinusoidal gratings, reversal checkerboards and oscillating expansion-contraction concentric-rings) of SSVEP acuity assessment. METHODS: We tested subjective visual acuity both by tumbling E and Freiburg Visual Acuity and Contrast Test (FrACT) in 11 subjects. SSVEPs were induced by 11 spatial frequencies for each paradigm, and then a threshold determination criterion was used to define the objective SSVEP visual acuity. RESULTS: After SSVEP signal analysis, we found there was difference in SSVEP response of harmonic components and no difference in sensitive electrode placement for the six paradigms. We selected six electrodes (PO3, POz, PO4, O1, Oz and O2) as the sensitive electrodes to use in data processing for each paradigm. The results showed that except for brief-onset vertical sinusoidal gratings, the correlation and agreement between objective SSVEP and subjective FrACT acuity were all quite good, demonstrating good performance in acuity detection for the rest five paradigms. CONCLUSION: Except for brief-onset vertical sinusoidal gratings, all the five stimulus paradigms of reverse vertical sinusoidal gratings, reverse horizontal sinusoidal gratings, reverse vertical square-wave gratings, reversal checkerboards and oscillating expansion-contraction concentric-rings performed quite well in objective SSVEP visual acuity assessment.

Assessment of Human Visual Acuity Using Visual Evoked Potential: A Review.

Visual evoked potential (VEP) has been used as an alternative method to assess visual acuity objectively, especially in non-verbal infants and adults with low intellectual abilities or malingering. By sweeping the spatial frequency of visual stimuli and recording the corresponding VEP, VEP acuity can be defined by analyzing electroencephalography (EEG) signals. This paper presents a review on the VEP-based visual acuity assessment technique, including a brief overview of the technique, the effects of the parameters of visual stimuli, and signal acquisition and analysis of the VEP acuity test, and a summary of the current clinical applications of the technique. Finally, we discuss the current problems in this research domain and potential future work, which may enable this technique to be used more widely and quickly, deepening the VEP and even electrophysiology research on the detection and diagnosis of visual function.

Objective and quantitative assessment of visual acuity and contrast sensitivity based on steady-state motion visual evoked potentials using concentric-ring paradigm.

PURPOSE: The traditional assessment of visual acuity and contrast sensitivity depends more on subjective judgments. Steady-state motion visual evoked potentials (SSMVEPs) can provide an objective and quantitative method to evaluate visual functions such as visual acuity and contrast sensitivity. Here, we explored the possibility of objective SSMVEP visual acuity and contrast sensitivity testing, and compared its performance with that of psychophysical methods. METHODS: In this study, we designed a specific concentric ring with oscillating expansion and contraction SSMVEP paradigm to assess visual acuity and contrast sensitivity. By changing the parameters of the paradigm, the SSMVEP paradigm with different contrasts and spatial frequencies corresponding to different visual acuity and contrast sensitivity was designed. Moreover, we proposed a threshold determination criterion to define the corresponding objective SSMVEP visual acuity and contrast sensitivity. RESULTS: We tested visual acuity and contrast sensitivity of sixteen healthy adults utilizing this paradigm with an electroencephalography system. Our data suggested that there was no significant difference between objective visual acuity and contrast sensitivity measurements based on the SSMVEPs and subjective psychophysical ones. CONCLUSION: Our study proved that SSMVEPs can be an objective and quantitative method to measure visual acuity and contrast sensitivity.

Process-based network decomposition reveals backbone motif structure.

A central challenge in systems biology today is to understand the network of interactions among biomolecules and, especially, the organizing principles underlying such networks. Recent analysis of known networks has identified small motifs that occur ubiquitously, suggesting that larger networks might be constructed in the manner of electronic circuits by assembling groups of these smaller modules. Using a unique process-based approach to analyzing such networks, we show for two cell-cycle networks that each of these networks contains a giant backbone motif spanning all the network nodes that provides the main functional response. The backbone is in fact the smallest network capable of providing the desired functionality. Furthermore, the remaining edges in the network form smaller motifs whose role is to confer stability properties rather than provide function. The process-based approach used in the above analysis has additional benefits: It is scalable, analytic (resulting in a single analyzable expression that describes the behavior), and computationally efficient (all possible minimal networks for a biological process can be identified and enumerated).

Frequency Domain Filtering Method for SSVEP-EEG Preprocessing.

Steady-state visual evoked potential (SSVEP) signal collected from the scalp typically contains other types of electric signals, and it is important to remove these noise components from the actual signal by application of a pre-processing step for accurate analysis. High-pass or bandpass filtering of the SSVEP signal in the time domain is the most common pre-processing method. Because frequency is the most important feature information contained in the SSVEP signal, a technique for frequency-domain filtering of SSVEP was proposed here. In this method, the time-domain signal is extended to multi-dimensional signal by empirical mode decomposition (EMD), where each dimension represents a intrinsic mode function (IMF). The multi-dimensional signal is transformed to a frequency-domain signal by 2-D Fourier transform, the Gaussian high-pass filter function is constructed to perform high-pass filtering, and then the filtered signal is transformed to time domain by 2-D inverse Fourier transform. Finally, the filtered multi-dimensional intrinsic mode function is superimposed and averaged as the frequency-domain filtered signal. Compared with the time-domain filtering method, the experimental results revealed that frequency-domain filtering method effectively removed the baseline drift in signal and effectively suppressed the low-frequency interference component. This method was tested using data from public datasets and the results show that the proposed frequency-domain filtering method can significantly improve the feature recognition performance of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA) methods. Thus, the results suggest that the application of frequency-domain filtering in the pre-processing stage allows improved noise removal. The proposed method extends SSVEP signal filtering from time-domain to frequency-domain, and the results suggest that this analysis tool significantly promotes the practical application of SSVEP systems.

A Precise Frequency Recognition Method of Short-Time SSVEP Signals Based on Signal Extension.

OBJECTIVE: Improving the Information Transfer Rate (ITR) is a popular research topic in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The higher recognition accuracy of short-time SSVEP signal is critical to improving ITR and achieving high-speed SSVEP-BCIs. However, the existing algorithms have unsatisfactory performance on recognizing short-time SSVEP signals, especially for calibration-free methods. METHOD: This study for the first time proposed improving the recognition accuracy of short-time SSVEP signals based on the calibration-free method by extending the SSVEP signal length. A signal extension model based on Multi-channel adaptive Fourier decomposition with different Phase (DP-MAFD) is proposed to achieve signal extension. Then the Canonical Correlation Analysis based on signal extension (SE-CCA) is proposed to complete the recognition and classification of SSVEP signals after extension. RESULT: The similarity study and SNR comparison analysis on public SSVEP datasets demonstrate that the proposed signal extension model has the ability to extend SSVEP signals. The classification results show that the proposed method outperforms Canonical Correlation Analysis (CCA) and Filter Bank Canonical Correlation Analysis (FBCCA) significantly in the measure of classification accuracy and information transmission rate (ITR), especially for short-time signals. The highest ITR of SE-CCA is improved to 175.61 bits/min at around 1s, while CCA is 100.55 bits/min at 1.75s and FBCCA is 141.76 bits/min at 1.25s. CONCLUSION: The signal extension method can improve the recognition accuracy of short-time SSVEP signals and further improve the ITR of SSVEP-BCIs.

Cross-subject spatial filter transfer method for SSVEP-EEG feature recognition.

Objective.Steady-state visual evoked potential (SSVEP) is an important control method of the brain-computer interface (BCI) system. The development of an efficient SSVEP feature decoding algorithm is the core issue in SSVEP-BCI. It has been proposed to use user training data to reduce the spontaneous electroencephalogram activity interference on SSVEP response, thereby improving the feature recognition accuracy of the SSVEP signal. Nevertheless, the tedious data collection process increases the mental fatigue of the user and severely affects the applicability of the BCI system.Approach.A cross-subject spatial filter transfer (CSSFT) method that transfer the existing user model with good SSVEP response to the new user test data without collecting any training data from the new user is proposed.Main results.Experimental results demonstrate that the transfer model increases the distinction of the feature discriminant coefficient between the gaze following target and the non-gaze following target and accurately identifies the wrong target in the fundamental algorithm model. The public datasets show that the CSSFT method significantly increases the recognition performance of canonical correlation analysis (CCA) and filter bank CCA. Additionally, when the data used to calculate the transfer model contains one data block only, the CSSFT method retains its effective feature recognition capabilities.Significance.The proposed method requires no tedious data calibration process for new users, provides an effective technical solution for the transfer of the cross-subject model, and has potential application value for promoting the application of the BCI system.

An improved cross-subject spatial filter transfer method for SSVEP-based BCI.

Objective.Steady-state visual evoked potential (SSVEP) training feature recognition algorithms utilize user training data to reduce the interference of spontaneous electroencephalogram activities on SSVEP response for improved recognition accuracy. The data collection process can be tedious, increasing the mental fatigue of users and also seriously affecting the practicality of SSVEP-based brain-computer interface (BCI) systems.Approach. As an alternative, a cross-subject spatial filter transfer (CSSFT) method to transfer an existing user data model with good SSVEP response to new user test data has been proposed. The CSSFT method uses superposition averages of data for multiple blocks of data as transfer data. However, the amplitude and pattern of brain signals are often significantly different across trials. The goal of this study was to improve superposition averaging for the CSSFT method and propose anEnsemblescheme based on ensemble learning, and anExpansionscheme based on matrix expansion.Main results. The feature recognition performance was compared for CSSFT and the proposed improved CSSFT method using two public datasets. The results demonstrated that the improved CSSFT method can significantly improve the recognition accuracy and information transmission rate of existing methods.Significance.This strategy avoids a tedious data collection process, and promotes the potential practical application of BCI systems.

SSVEP-EEG Denoising via Image Filtering Methods.

Steady-state visual evoked potential (SSVEP) is widely used in electroencephalogram (EEG) control, medical detection, cognitive neuroscience, and other fields. However, successful application requires improving the detection performance of SSVEP signal frequency characteristics. Most strategies to enhance the signal-to-noise ratio of SSVEP utilize application of a spatial filter. Here, we propose a method for image filtering denoising (IFD) of the SSVEP signal from the perspective of image analysis, as a preprocessing step for signal analysis. Arithmetic mean, geometric mean, Gaussian, and non-local means filtering methods were tested, and the experimental results show that image filtering of SSVEP cannot effectively remove the noise. Thus, we proposed a reverse solution in which the SSVEP noise signal was obtained by image filtering, and then the noise was subtracted from the original signal. Comparison of the recognition accuracy of the SSVEP signal before and after denoising was used to evaluate the denoising performance for stimuli of different duration. After IFD processing, SSVEP exhibited higher recognition accuracy, indicating the effectiveness of this proposed denoising method. Application of this method improves the detection performance of SSVEP signal frequency characteristics, combines image processing and brain signal analysis, and expands the research scope of brain signal analysis for widespread application.

Enhancing detection of steady-state visual evoked potentials using channel ensemble method.

Objective.This study proposed and evaluated a channel ensemble approach to enhance detection of steady-state visual evoked potentials (SSVEPs).Approach.Collected multi-channel electroencephalogram signals were classified into multiple groups of new analysis signals based on correlation analysis, and each group of analysis signals contained signals from a different number of electrode channels. These groups of analysis signals were used as the input of a training-free feature extraction model, and the obtained feature coefficients were converted into feature probability values using thesoftmaxfunction. The ensemble value of multiple sets of feature probability values was determined and used as the final discrimination coefficient.Main results.Compared with canonical correlation analysis, likelihood ratio test, and multivariate synchronization index analysis methods using a standard approach, the recognition accuracies of the methods using a channel ensemble approach were improved by 5.05%, 3.87%, and 3.42%, and the information transfer rates (ITRs) were improved by 6.00%, 4.61%, and 3.71%, respectively. The channel ensemble method also obtained better recognition results than the standard algorithm on the public dataset. This study validated the efficiency of the proposed method to enhance the detection of SSVEPs, demonstrating its potential use in practical brain-computer interface (BCI) systems.Significance. A SSVEP-based BCI system using a channel ensemble method could achieve high ITR, indicating great potential of this design for various applications with improved control and interaction.

Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering.

The purpose of this study was to enhance the performance of steady-state visual evoked potential (SSVEP)-based visual acuity assessment with spatial filtering methods. Using the vertical sinusoidal gratings at six spatial frequency steps as the visual stimuli for 11 subjects, SSVEPs were recorded from six occipital electrodes (O1, Oz, O2, PO3, POz, and PO4). Ten commonly used training-free spatial filtering methods, i.e., native combination (single-electrode), bipolar combination, Laplacian combination, average combination, common average reference (CAR), minimum energy combination (MEC), maximum contrast combination (MCC), canonical correlation analysis (CCA), multivariate synchronization index (MSI), and partial least squares (PLS), were compared for multielectrode signals combination in SSVEP visual acuity assessment by statistical analyses, e.g., Bland-Altman analysis and repeated-measures ANOVA. The SSVEP signal characteristics corresponding to each spatial filtering method were compared, determining the chosen spatial filtering methods of CCA and MSI with a higher performance than the native combination for further signal processing. After the visual acuity threshold estimation criterion, the agreement between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for the native combination (0.253 logMAR), CCA (0.202 logMAR), and MSI (0.208 logMAR) was all good, and the difference between FrACT and SSVEP visual acuity was also all acceptable for the native combination (-0.095 logMAR), CCA (0.039 logMAR), and MSI (-0.080 logMAR), where CCA-based SSVEP visual acuity had the best performance and the native combination had the worst. The study proved that the performance of SSVEP-based visual acuity can be enhanced by spatial filtering methods of CCA and MSI and also recommended CCA as the spatial filtering method for multielectrode signals combination in SSVEP visual acuity assessment.

Weak Feature Extraction and Strong Noise Suppression for SSVEP-EEG Based on Chaotic Detection Technology.

Brain computer interface (BCI) is a novel communication method that does not rely on the normal neural pathway between the brain and muscle of human. It can transform mental activities into relevant commands to control external equipment and establish direct communication pathway. Among different paradigms, steady-state visual evoked potential (SSVEP) is widely used due to its certain periodicity and stability of control. However, electroencephalogram (EEG) of SSVEP is extremely weak and companied with multi-scale and strong noise. Existing algorithms for classification are based on the principle of template matching and spatial filtering, which cannot obtain satisfied performance of feature extraction under the multi-scale noise. Especially for the subjects produce weak response for external stimuli in EEG representation, i.e., BCI-Illiteracy subject, traditional algorithms are difficult to recognize the internal patterns of brain. To address this issue, a novel method based on Chaos theory is proposed to extract feature of SSVEP. The rule of this method is applying the peculiarity of nonlinear dynamics system to detect feature of SSVEP by judging the state changes of chaotic systems after adding weak EEG. To evaluate the validity of proposed method, this research recruit 32 subjects to participate the experiment. All subjects are divided into two groups according to the preliminary classification accuracy (mean acc >70% or < 70%) by canonical correlation analysis and we define the accuracy above 70% as group A (normal subjects), below 70% as group B (BCI-Illiteracy). Then, the classification accuracy and information transmission rate of two groups are verified using Chaotic theory. Experimental results show that all classification methods using in our study achieve good performance for normal subjects while chaos obtain excellent performance and significant improvements than traditional methods for BCI-Illiteracy.

Enhancement of capability for motor imagery using vestibular imbalance stimulation during brain computer interface.

Objective.Motor imagery (MI), based on the theory of mirror neurons and neuroplasticity, can promote motor cortical activation in neurorehabilitation. The strategy of MI based on brain-computer interface (BCI) has been used in rehabilitation training and daily assistance for patients with hemiplegia in recent years. However, it is difficult to maintain the consistency and timeliness of receiving external stimulation to neural activation in most subjects owing to the high variability of electroencephalogram (EEG) representation across trials/subjects. Moreover, in practical application, MI-BCI cannot highly activate the motor cortex and provide stable interaction owing to the weakness of the EEG feature and lack of an effective mode of activation.Approach.In this study, a novel hybrid BCI paradigm based on MI and vestibular stimulation motor imagery (VSMI) was proposed to enhance the capability of feature response for MI. Twelve subjects participated in a group of controlled experiments containing VSMI and MI. Three indicators, namely, activation degree, timeliness, and classification accuracy, were adopted to evaluate the performance of the task.Main results.Vestibular stimulation could significantly strengthen the suppression ofαandßbands of contralateral brain regions during MI, that is, enhance the activation degree of the motor cortex (p< 0.01). Compared with MI, the timeliness of EEG feature-response achieved obvious improvements in VSMI experiments. Moreover, the averaged classification accuracy of VSMI and MI was 80.56% and 69.38%, respectively.Significance.The experimental results indicate that specific vestibular activity contributes to the oscillations of the motor cortex and has a positive effect on spontaneous imagery, which provides a novel MI paradigm and enables the preliminary exploration of sensorimotor integration of MI.

Does Oblique Effect Affect SSVEP-Based Visual Acuity Assessment?

This study aimed to explore whether there was an effect on steady-state visual evoked potential (SSVEP) visual acuity assessment from the oblique effect or the stimulus orientation. SSVEPs were induced by seven visual stimuli, e.g., the reversal sinusoidal gratings with horizontal, two oblique, and vertical orientations, reversal checkerboards with vertical and oblique orientations, and oscillating expansion-contraction concentric-rings, at six spatial frequency steps. Ten subjects participated in the experiment. Subsequently, a threshold estimation criterion was used to determine the objective SSVEP visual acuity corresponding to each visual stimulus. Taking the SSVEP amplitude and signal-to-noise-ratio (SNR) of the fundamental reversal frequency as signal characteristics, both the SSVEP amplitude and SNR induced by the reversal sinusoidal gratings at 3.0 cpd among four stimulus orientations had no significant difference, and the same finding was also shown in the checkerboards between vertical and oblique orientation. In addition, the SSVEP visual acuity obtained by the threshold estimation criterion for all seven visual stimuli showed no significant difference. This study demonstrated that the SSVEPs induced by all these seven visual stimuli had a similarly good performance in evaluating visual acuity, and the oblique effect or the stimulus orientation had little effect on SSVEP response as well as the SSVEP visual acuity.

Real-time, precise, rapid and objective visual acuity assessment by self-adaptive step SSVEPs.

Objective. This study aimed to explore an online, real-time, and precise method to assess steady-state visual evoked potential (SSVEP)-based visual acuity more rapidly and objectively with self-adaptive spatial frequency steps.Approach. Taking the vertical sinusoidal reversal gratings with different spatial frequencies and temporal frequencies as the visual stimuli, according to the psychometric function for visual acuity assessment, a self-adaptive procedure, the best parameter estimation by sequential testing algorithm, was used to calculate the spatial frequency sequence based on all the previous spatial frequencies and their significance of the SSVEP response. Simultaneously, the canonical correlation analysis (CCA) method with a signal-to-noise ratio (SNR) significance detection criterion was used to judge the significance of the SSVEP response.Main results.After 18 iterative trails, the spatial frequency to be presented converged to a value, which was exactly defined as the SSVEP visual acuity threshold. Our results indicated that this SSVEP acuity had a good agreement and correlation with subjective Freiburg Visual Acuity and Contrast Test acuity, and the test-retest repeatability was also good.Significance. The self-adaptive step SSVEP procedure combined with the CCA method and SNR significance detection criterion appears to be an alternative method in the real-time SSVEP acuity test to obtain objective visual acuity more rapidly and precisely.