Source Contributions to PM2.5 under Unfavorable Weather Conditions in Guangzhou City,China

Historical haze episodes（2013–16） in Guangzhou were examined and classified according to synoptic weather systems.Four types of weather systems were found to be unfavorable, among which ＂foreside of a cold front＂（FC） and ＂sea high pressure＂（SP） were the most frequent（〉 75% of the total）. Targeted case studies were conducted based on an FC-affected event and an SP-affected event with the aim of understanding the characteristics of the contributions of source regions to fine particulate matter（PM（2.5）） in Guangzhou. Four kinds of contributions—namely, emissions outside Guangdong Province（super-region）, emissions from the Pearl River Delta region（PRD region）, emissions from Guangzhou–Foshan–Shenzhen（GFS region）, and emissions from Guangzhou（local）—were investigated using the Weather Research and Forecasting–Community Multiscale Air Quality model. The results showed that the source region contribution differed with different weather systems. SP was a stagnant weather condition, and the source region contribution ratio showed that the local region was a major contributor（37%）, while the PRD region, GFS region and the super-region only contributed 8%, 2.8% and 7%, respectively, to PM（2.5） concentrations. By contrast, FC favored regional transport. The super-region became noticeable,contributing 34.8%, while the local region decreased to 12%. A simple method was proposed to quantify the relative impact of meteorology and emissions. Meteorology had a 35% impact, compared with an impact of-18% for emissions, when comparing the FC-affected event with that of the SP. The results from this study can provide guidance to policymakers for the implementation of effective control strategies.

Pupillometry Analysis of Rapid Serial Visual Presentation at Five Presentation Rates

In this study,the effect of presentation rates on pupil dilation is investigated for target recognition in the Rapid Serial Visual Presentation(RSVP)paradigm.In this experiment,the RSVP paradigm with five different presentation rates,including 50,80,100,150,and 200 ms,is designed.The pupillometry data of 15 subjects are collected and analyzed.The pupillometry results reveal that the peak and average amplitudes for pupil size and velocity at the 80-ms presentation rate are considerably higher than those at other presentation rates.The average amplitude of pupil acceleration at the 80-ms presentation rate is significantly higher than those at the other presentation rates.The latencies under 50-and 80-ms presentation rates are considerably lower than those of 100-,150-,and 200-ms presentation rates.Additionally,no considerable differences are observed in the peak,average amplitude,and latency of pupil size,pupil velocity,and acceleration under 100-,150-,and 200-ms presentation rates.These results reveal that with the increase in the presentation rate,pupil dilation first increases,then decreases,and later reaches saturation.The 80-ms presentation rate results in the largest point of pupil dilation.No correlation is observed between pupil dilation and recognition accuracy under the five presentation rates.

Boosting the Information Transfer Rate of an SSVEP-BCI System Using Maximal-Phase-Locking Value and Minimal-Distance Spatial Filter Banks

For Brain-Computer Interface(BCI) systems, improving the Information Transfer Rate(ITR) is a very critical issue. This study focuses on a Steady-State Visually Evoked Potential(SSVEP)-based BCI because of its advantage of high ITR. Unsupervised Canonical Correlation Analysis(CCA)-based method has been widely employed because of its high efficiency and easy implementation. In a recent study, an ensemble-CCA method based on individual training data was proposed and achieved an excellent performance with ITR of 267 bit/min.A 40-target SSVEP-BCI speller was investigated in this study, using an integration of Minimal-Distance(MD) and Maximal-Phase-locking value(MP) approaches. In the MD approach, a spatial filter is developed to minimize the distance between the training data and the reference sine signal, and in this study, two different types of distance were compared. In the MP approach, a spatial filter is developed to maximize the Phase-Locking Value(PLV)between the training calibration data and the reference sine signal. In addition to the fundamental frequency of stimulation, the harmonics were used to train MD and MP spatial filters, which formed spatial filter banks. The test data epoch was multiplied by the MP and MD spatial filter banks, and the distances and PLVs were extracted as features for recognition. Across 12 subjects with a 0.4 s-data length, the proposed method realized an average classification accuracy and ITR of 93% and 307 bit/min, respectively, which is significantly higher than the current state-of-the-art method. To the best of our knowledge, these results suggest that the proposed method has achieved the highest ITR in SSVEP-BCI studies.

Study on Robot Grasping System of SSVEP-BCI Based on Augmented Reality Stimulus

Although notable progress has been made in the study of Steady-State Visual Evoked Potential(SSVEP)-based Brain-Computer Interface(BCI),several factors that limit the practical applications of BCIs still exist.One of these factors is the importability of the stimulator.In this study,Augmented Reality(AR)technology was introduced to present the visual stimuli of SSVEP-BCI,while the robot grasping experiment was designed to verify the applicability of the AR-BCI system.The offline experiment was designed to determine the best stimulus time,while the online experiment was used to complete the robot grasping task.The offline experiment revealed that better information transfer rate performance could be achieved when the stimulation time is 2 s.Results of the online experiment indicate that all 12 subjects could control the robot to complete the robot grasping task,which indicates the applicability of the AR-SSVEP-humanoid robot(NAO)system.This study verified the reliability of the AR-BCI system and indicated the applicability of the AR-SSVEP-NAO system in robot grasping tasks.

Cross-Target Transfer Algorithm Based on the Volterra Model of SSVEP-BCI

In general, a large amount of training data can effectively improve the classification performance of the Steady-State Visually Evoked Potential(SSVEP)-based Brain-Computer Interface(BCI) system. However, it will prolong the training time and considerably restrict the practicality of the system. This study proposed a SSVEP nonlinear signal model based on the Volterra filter, which could reconstruct stable reference signals using relatively small number of training targets by transfer learning, thereby reducing the training cost of SSVEP-BCI. Moreover,this study designed a transfer-extended Canonical Correlation Analysis(t-eCCA) method based on the model to achieve cross-target transfer. As a result, in a single-target SSVEP experiment with 16 stimulus frequencies,t-eCCA obtained an average accuracy of 86.96%˙12.87% across 12 subjects using only half of the calibration time,which exhibited no significant difference from the representative training classification algorithms, namely, extended canonical correlation analysis(88.32%±13.97%) and task-related component analysis(88.92%±14.44%), and was significantly higher than that of the classic non-training algorithms, namely, Canonical Correlation Analysis(CCA) as well as filter-bank CCA. Results showed that the proposed cross-target transfer algorithm t-eCCA could fully utilize the information about the targets and its stimulus frequencies and effectively reduce the training time of SSVEP-BCI.

Studying the Effect of the Pre-Stimulation Paradigm on Steady-State Visual Evoked Potentials with Dynamic Models Based on the Zero-Pole Analytical Method

This study explored methods for improving the performance of Steady-State Visual Evoked Potential(SSVEP)-based Brain-Computer Interfaces(BCI), and introduced a new analytical method to quantitatively analyze and reflect the characteristics of SSVEP. We focused on the effect of the pre-stimulation paradigm on the SSVEP dynamic models and the dynamic response process of SSVEP, and performed a comparative analysis of three pre-stimulus paradigms(black, gray, and white). Four dynamic models with different orders(second-and third-order)and with and without a zero point were used to fit the SSVEP envelope. The zero-pole analytical method was adopted to conduct quantitative analysis on the dynamic models, and the response characteristics of SSVEP were represented by zero-pole distribution characteristics. The results of this study indicated that the pre-stimulation paradigm affects the characteristics of SSVEP, and the dynamic models had good fitting abilities with SSVEPs under various types of pre-stimulation. Furthermore, the zero-pole characteristics of the models effectively characterize the damping coefficient, oscillation period, and other SSVEP characteristics. The comparison of zeros and poles indicated that the gray pre-stimulation condition corresponds to a lower damping coefficient, thus showing its potential to improve the performance of SSVEP-BCIs.

Sensitivity of N400 Effect During Speech Comprehension Under the Uni-and Bi-Modality Conditions

N400 is an objective electrophysiological index in semantic processing for brain.This study focuses on the sensitivity of N400 effect during speech comprehension under the uni-and bi-modality conditions.Varying the Signal-to-Noise Ratio(SNR) of speech signal under the conditions of Audio-only(A),Visual-only(V,i.e.,lip-reading),and Audio-Visual(AV),the semantic priming paradigm is used to evoke N400 effect and measure the speech recognition rate.For the conditions A and high SNR AV,the N400 amplitudes in the central region are larger;for the conditions of V and low SNR AV,the N400 amplitudes in the left-frontal region are larger.The N400 amplitudes of frontal and central regions under the conditions of A,AV,and V are consistent with speech recognition rate of behavioral results.These results indicate that audio-cognition is better than visual-cognition at high SNR,and visual-cognition is better than audio-cognition at low SNR.

Promoting brain-computer interface in China by BCI Controlled Robot Contest in World Robot Contest

The year ahead marks the 50th anniversary of the first publication on the brain-computer interface(BCI)proposed by Vidal in 1973.Over the past decades,continuous progress has been made in designing and optimizing the BCI system toward a high-performance,robust and ease-of-use“mind reading”.The nascent field of BCI has brought together scientists from neuroscience,computer science as well as engineers and clinicians worldwide to address the highly challenging problem.

Event-Related Potentials Analysis on Perception of Moving Object at Different Speeds

For this research,electroencephalography(EEG)was analyzed to investigate the perception ability of the brain for moving objects at different speeds.In this experiment,total six kinds of videos regarding license plates were created,moving at distinct speed of 0.26 m/s,0.36 m/s,0.46 m/s,0.56 m/s,0.66 m/s,and 0.76 m/s,respectively.In the semantic priming paradigm,the N400 effect was analyzed for each speed.The ERP results demonstrated that the N400 amplitude gradually reduced with increasing speed.At the three lower speeds,N400 was evoked evidently and mainly distributed in the centro-posterior region.At the three higher speeds,no significant N400 effect was found.The results concluded that the perception ability of the brain declined with the acceleration of the object’s moving speed and that the brain recognized the detailed information of the moving object when its speed was lower than 0.46 m/s.

The effect of fatigue on brain connectivity networks

In the fatigue state,the neural response characteristics of the brain might be different from those in the normal state.Brain functional connectivity analysis is an effective tool for distinguishing between different brain states.For example,comparative studies on the brain functional connectivity have the potential to reveal the functional differences in different mental states.The purpose of this study was to explore the relationship between human mental states and brain control abilities by analyzing the effect of fatigue on the brain response connectivity.In particular,the phasescrambling method was used to generate images with two noise levels,while the N-back working memory task was used to induce the fatigue state in subjects.The paradigm of rapid serial visual presentation(RSVP)was used to present visual stimuli.The analysis of brain connections in the normal and fatigue states was conducted using the open-source e Connectome toolbox.The results demonstrated that the control areas of neural responses were mainly distributed in the parietal region in both the normal and fatigue states.Compared to the normal state,the brain connectivity power in the parietal region was significantly weakened under the fatigue state,which indicates that the control ability of the brain is reduced in the fatigue state.

BCI Controlled Robot Contest on the 50th Anniversary of Brain-Computer Interfaces

Introduction Is it feasible to employ neural signals for the purpose of communication?In 1938,this thoughtprovoking query was visually embodied in a sketch received by Hans Berger,a German Professor of Psychiatry renowned for his discovery of human electroencephalography(EEG)nearly a century ago.In this fanciful sketch.