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.

基于SRIO的数据传输设计

为解决大数据量传输系统中传输速率低、带宽小、可靠性差的问题,提出一种基于SRIO协议的FPGA间大量数据高速传输的设计方案。设计使用FPGA内嵌SRIO IP核对传输数据进行发送、解析和接收;使用FPGA内部GTP高速串行收发器作为物理层传输基础;利用光电转换模块实现光、电信号的转换,以完成数据远距离传输。通过测试验证本设计数据传输准确可靠,传输速率可达280 MB/s,且本方案已成功应用于遥测系统存储器地面测试台项目,可实现两FPGA设备间大量数据高速可靠传输。

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.

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.