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.

A review of deep learning methods for cross-subject rapid serial visual presentation detection in World Robot Contest 2022

The rapid serial visual presentation(RSVP)paradigm has garnered considerable attention in brain–computer interface(BCI)systems.Studies have focused on using cross-subject electroencephalogram data to train cross-subject RSVP detection models.In this study,we performed a comparative analysis of the top 5 deep learning algorithms used by various teams in the event-related potential competition of the BCI Controlled Robot Contest in World Robot Contest 2022.We evaluated these algorithms on the final data set and compared their performance in cross-subject RSVP detection.The results revealed that deep learning models can achieve excellent results with appropriate training methods when applied to cross-subject detection tasks.We discussed the limitations of existing deep learning algorithms in cross-subject RSVP detection and highlighted potential research directions.

An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task

As a new type of brain-computer interface(BCI),the rapid serial visual presentation(RSVP)paradigm has attracted significant attention.The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition.This paper proposed an improved EEGNet model to achieve good performance in offline and online data.Specifically,the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram(EEG)signals.The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples.Additionally,the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model.We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place.The average recall rate of the four participants reached 51.56%in triple classification.In the offline data benchmark dataset(64 subjects-RSVP tasks),the average recall rates of groups A and B reached 76.07%and 78.11%,respectively.We provided an alternative method to identify targets based on the RSVP paradigm.

Frequency of presenting visual acuity and visual impairment in Chinese college students

AIM:To obtain the baseline data on presenting visual acuity(PVA)and evaluate the prevalence and associated factors for visual impairment based on PVA in 9070 Chinese college students.METHODS:The freshmen at a university in southern China,including 6527 undergraduate students and 2543 graduate students,were investigated for some sociodemographic characteristics and underwent routine medical examination,including measuring PVA,height,and weight.Visual impairment was defined according to the new World Health Organization criteria for blindness and visual impairment.RESULTS:In 9070 college students,the mean PVA in the better eye was 0.094±0.163 log MAR.The prevalence of visual impairment based on PVA was 2.7%.Only 38.3%college students had normal visual acuity[PVA equal to 0 log MAR(20/20)in both eyes].There were 69.8%of students wearing spectacles.Logistic regression showed that home region(non-Guangdong provinces,P<0.0001,OR=1.70)was risk factor for visual impairment while BMI(P=0.001,OR=0.92)was protective factor from visual impairment.Ethnicity(Han Chinese,P<0.0001,OR=3.17)was risk factor for wearing spectacles while age(P=0.01,OR=0.90)was protective factor from wearing spectacles.CONCLUSION:This study provides the baseline data on PVA and the prevalence of visual impairment in Chinese college students.Our analyses reveal that BMI and home region are associated factors for visual impairment based on PVA,while age and ethnicity are associated factors for wearing spectacles.

Visual acuity of urban and rural adults in a coastal province of southern China:the Fujian Eye Study

AIM:To evaluate the vision status and sociodemographic associations of visual acuity(VA)in an urban and rural population in a coastal province of southern China.METHODS:The Fujian Eye Study,a population-based cross-sectional study,was performed from May 2018 to October 2019.Totally 10044 participants over 50 years old from all nine cities in Fujian Province were enrolled,and underwent a questionnaire and a series of standard physical and ocular examinations.VA was measured by E Standard Logarithmic Visual Acuity Chart(GB 11533-1989).Data was double entered with Epi Data v3.1 for data collation and Stata/SE statistical software v15.1 was used to analyze the data.RESULTS:Totally 8211(81.8%)participants were finally included and were divided into urban populations(4678 subjects),rural populations(n=3533),coastal residents(n=6434),and inland residents(1777 subjects);4836 participants were female.The mean age was 64.39±8.87 y(median 64 y;range 50-98 y).The mean presenting VA was 0.61±0.30(0.23±0.27 log MAR),and the mean best correctedvisual acuity(BCVA)was 0.82±0.28(0.08±0.19 log MAR).In the multiple regression analysis,BCVA was significantly correlated with several socioeconomic and biologic factors,including age(P<0.001),education level(P<0.001),income(P=0.005),rural residency(P<0.001),inland residency(P=0.001)and refractive error(P<0.001),while sex(P=0.194)was independent with BCVA.CONCLUSION:Accessible services and eye health policies targeting the elderly,people with high myopia and people living in rural or inland areas are needed.

Review of training-free event-related potential classification approaches in the World Robot Contest 2021

Recently,rapid serial visual presentation(RSVP),as a new event-related potential(ERP)paradigm,has become one of the most popular forms in electroencephalogram signal processing technologies.Several improvement approaches have been proposed to improve the performance of RSVP analysis.In brain-computer interface systems based on RSVP,the family of approaches that do not depend on training specific parameters is essential.The participating teams proposed several effective training-free frameworks of algorithms in the ERP competition of the BCI Controlled Robot Contest in World Robot Contest 2021.This paper discusses the effectiveness of various approaches in improving the performance of the system without requiring training and suggests how to apply these approaches in a practical system.First,appropriate preprocessing techniques will greatly improve the results.Then,the non-deep learning algorithm may be more stable than the deep learning approach.Furthermore,ensemble learning can make the model more stable and robust.

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.

Human Body Pose Recognition System Based on Teaching Interaction

In view of the problems of the high time cost and low accuracy ofmanual supervision in traditional classroom teaching, this paper proposes a humanbody pose recognition system based on teaching interaction. The enhanced basicnetwork (ResNext-101+FPN)was used in Mask R-CNN to extract the features ofthe input images. Then based on the behavior analysis algorithm and face detectiondata, the behavior data of each student in the classroom were obtained. Moreover,the behavior data were applied to support multi-dimensional visualization. Theexperimental results show that the system can timely and effectively reflect thelearning status of students, and help teachers accurately grasp the classroom learningstate of students, so as to adjust teaching strategies in a targeted way and helpimprove the quality of teaching.