Observing the steady-state visual evoked potentials with a compact quad-channel spin exchange relaxation-free magnetometer

We observed the steady-state visually evoked potential(SSVEP) from a healthy subject using a compact quad-channel potassium spin exchange relaxation-free(SERF) optically pumped magnetometer(OPM). To this end, 30 s of data were collected, and SSVEP-related magnetic responses with signal intensity ranging from 150 fT to 300 f T were observed for all four channels. The corresponding signal to noise ratio(SNR) was in the range of 3.5–5.5. We then used different channels to operate the sensor as a gradiometer. In the specific case of detecting SSVEP, we noticed that the short channel separation distance led to a strongly diminished gradiometer signal. Although not optimal for the case of SSVEP detection, this set-up can prove to be highly useful for other magnetoencephalography(MEG) paradigms that require good noise cancellation.Considering its compactness, low cost, and good performance, the K-SERF sensor has great potential for biomagnetic field measurements and brain-computer interfaces(BCI).

Influence of stimuli color on steady-state visual evoked potentials based BCI wheelchair control

In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attention. This study tries to develop a SSVEP based BCI system that can control a wheelchair prototype in five different positions including stop position. In this study four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using Lab-VIEW. Four stimuli colors, green, red, blue and violet were used to investigate the color influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were segmented into 1 second window and features were extracted by using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass SVM, is used to classify SSVEP signals. During stimuli color comparison SSVEP with violet color showed higher accuracy than that with green, red and blue stimuli.

A Secure Cryptographic System Based on Steady-State Visual Evoked Potential Brain-Computer Interface Technology

Addressing the vulnerability of contact-based keyboard password systems to disclosure,this paper proposes and validates the feasibility of a non-contact secure password system based on brain-computer interface(BCI)technology that detects steady-state visual evoked potential(SSVEP)signals.The system first lets a testee look at a digital stimulus source flashing at a specific frequency,and uses a wearable dry electrode sensor to collect the SSVEP signal.Secondly,a canonical correlation analysis method is applied to analyze the frequency of the stimulus source that the testee is looking at,and feeds back a code result through headphones.Finally,after all password codes are input,the system makes a judgment and provides visual feedback to the testee.Experiments were conducted to test the accuracy of the system,where twelve stimulus target frequencies between 10-16Hz were selected within the easily recognizable flicker frequency range of human brain,and each of them was tested for 12 times.The results demonstrate that this SSVEP-BCI-based system is feasible,achieving an average accuracy rate of 97.2%,and exhibits promising applications in various domains such as financial transactions and identity recognition.

Review of brain-computer interface based on steady-state visual evoked potential

The brain-computer interface(BCI)technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life.Steady-state visual evoked potential(SSVEP)is the most researched BCI experimental paradigm,which offers the advantages of high signal-to-noise ratio and short training-time requirement by users.In a complete BCI system,the two most critical components are the experimental paradigm and decoding algorithm.However,a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies.In the present study,the transient visual evoked potential,SSVEP,and various improved SSVEP paradigms are compared and analyzed,and the problems and development bottlenecks in the experimental paradigm are finally pointed out.Subsequently,the canonical correlation analysis and various improved decoding algorithms are introduced,and the opportunities and challenges of the SSVEP decoding algorithm are discussed.

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.

Effect of background luminance of visual stimulus on elicited steady-state visual evoked potentials

Steady-state visual evoked potential(SSVEP)-based brain-computer interfaces(BCIs)have been widely studied.Considerable progress has been made in the aspects of stimulus coding,electroencephalogram processing,and recognition algorithms to enhance system performance.The properties of SSVEP have been demonstrated to be highly sensitive to stimulus luminance.However,thus far,there have been very few reports on the impact of background luminance on the system performance of SSVEP-based BCIs.This study investigated the impact of stimulus background luminance on SSVEPs.Specifically,this study compared two types of background luminance,i.e.,(1)black luminance[red,green,blue(rgb):(0,0,0)]and(2)gray luminance[rgb:(128,128,128)],and determined their effect on the classification performance of SSVEPs at the stimulus frequencies of 9,11,13,and 15 Hz.The offline results from nine healthy subjects showed that compared with the gray background luminance,the black background luminance induced larger SSVEP amplitude and larger signal-to-noise ratio,resulting in a better classification accuracy.These results suggest that the background luminance of visual stimulus has a considerable effect on the SSVEP and therefore has a potential to improve the BCI performance.

Brain-computer control interface design for virtual household appliances based on steady-state visually evoked potential recognition

Brain–computer interface is a new form of interaction between humans and machines.This interaction helps the human brain control or operate external devices directly using electroencephalograph(EEG)signals.In this study,we first adopt a canonical correlation analysis method to find the stimulation frequency by calculating the correlation coefficient between the EEG data and multiple sets of harmonics with different frequencies.Then,we select the maximum correlation coefficient as the stimulus frequency and consequently identify steady-state visual evoked potentials.Afterward,we introduce power spectral density to adjust the stimulus frequency and a voting mechanism to reduce the false activation rate.Finally,we build a virtual household electrical appliance brain–computer control interface,which achieves over 72.84%accuracy for three classification problems.

AB053.Oscillatory activity specific to peripheral emotional treatment induced by a visual steady state

Background:Research suggests that the analysis of facial expressions by a healthy brain would take place approximately 170 ms after the presentation of a facial expression in the superior temporal sulcus and the fusiform gyrus,mostly in the right hemisphere.Some researchers argue that a fast pathway through the amygdala would allow automatic and early emotional treatment around 90 ms after stimulation.This treatment would be done subconsciously,even before this stimulus is perceived and could be approximated by presenting the stimuli quickly on the periphery of the fovea.The present study aimed to identify the neural correlates of a peripheral and simultaneous presentation of emotional expressions through a frequency tagging paradigm.Methods:The presentation of emotional facial expressions at a specific frequency induces in the visual cortex a stable and precise response to the presentation frequency[i.e.,a steady-state visual evoked potential(ssVEP)]that can be used as a frequency tag(i.e.,a frequency-tag to follow the cortical treatment of this stimulus.Here,the use of different specific stimulation frequencies allowed us to label the different facial expressions presented simultaneously and to obtain a reliable cortical response being associated with(I)each of the emotions and(II)the different times of presentations repeated(1/0.170 ms=~5.8 Hz,1/0.090 ms=~10.8 Hz).To identify the regions involved in emotional discrimination,we subtracted the brain activity induced by the rapid presentation of six emotional expressions of the activity induced by the presentation of the same emotion(reduced by neural adaptation).The results were compared to the hemisphere in which attention was sought,emotion and frequency of stimulation.Results:The signal-to-noise ratio of the cerebral oscillations referring to the treatment of the expression of fear was stronger in the regions specific to the emotional treatment when they were presented in the subjects peripheral vision,unbeknownst to them.In addition,the peripheral emotional treatment of fear at 10.8 Hz was associated with greater activation within the Gamma 1 and 2 frequency bands in the expected regions(frontotemporal and T6),as well as desynchronization in the Alpha frequency bands for the temporal regions.This modulation of the spectral power is independent of the attentional request.Conclusions:These results suggest that the emotional stimulation of fear presented in the peripheral vision and outside the attentional framework elicit an increase in brain activity,especially in the temporal lobe.The localization of this activity as well as the optimal stimulation frequency found for this facial expression suggests that it is treated by the fast pathway of the magnocellular layers.

An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady- state visual evoked paradigm

The radial contraction-expansion motion paradigm is a novel steady-state visual evoked experimental paradigm,and the electroencephalography(EEG)evoked potential is different from the traditional luminance modulation paradigm.The signal energy is concentrated chiefly in the fundamental frequency,while the higher harmonic power is lower.Therefore,the conventional steady-state visual evoked potential recognition algorithms optimizing multiple harmonic response components,such as the extended canonical correlation analysis(eCCA)and task-related component analysis(TRCA)algorithm,have poor recognition performance under the radial contraction-expansion motion paradigm.This paper proposes an extended binary subband canonical correlation analysis(eBSCCA)algorithm for the radial contraction-expansion motion paradigm.For the radial contraction-expansion motion paradigm,binary subband filtering was used to optimize the weighting coefficients of different frequency response signals,thereby improving the recognition performance of EEG signals.The results of offline experiments involving 13 subjects showed that the eBSCCA algorithm exhibits a better performance than the eCCA and TRCA algorithms under the stimulation of the radial contraction-expansion motion paradigm.In the online experiment,the average recognition accuracy of 13 subjects was 88.68%±6.33%,and the average information transmission rate(ITR)was 158.77±43.67 bits/min,which proved that the algorithm had good recognition effect signals evoked by the radial contraction-expansion motion paradigm.

一种基于两种不同范式的混合型脑-机接口系统

混合模式脑机接口是脑-机接口研究的一个新方向,它为进一步提高脑-机接口系统性能提供了可能。现有的混合模式脑-机接口所采用的范式通常需要借助较强的视觉刺激,容易引起受试者疲劳等问题。本研究提出将运动想象和运动起始时刻视觉诱发电位两种无需强烈视觉刺激的范式以串行的方式相结合,通过运动起始时刻视觉诱发电位控制字符的输入,通过运动想象控制界面的开关和允许输入下一字符,实现了一种可用于字符输入的混合模式脑-机接口系统。为了验证系统的可行性,共完成了5例实验。实验中受试者首先进行两种范式的训练,然后进行开关系统界面和输入字符的测试。实验结果显示,经过一定训练的受试可以较好地完成系统的操作,运动想象单步操作平均时间最短为3.9 s,字符输入的正确率最高可达93.3%。除了不容易令受试者产生疲劳外,本系统相比单一感觉模式的脑-机接口也具有可完成任务种类多、控制方式灵活等优势。

基于运动想象的脑机接口关键技术研究

基于运动想象的脑机接口技术,被广泛的认为是最具有前景的一类脑机接口,但目前在该技术的发展过程中存在着一系列急需解决的问题。本文从信号获取、特征提取、模式分类、在线系统等方面,介绍了一些解决问题的方法,特别是基于笔者长期研究工作的系列解决方案。如信号获取中能够去除低频偏置的放大器设计方法;特征识别方面针对在运动想象脑机接口中广泛使用的共空间模式方法的改进算法,使其具有更强的抗噪音能力;模式识别方面所提出的基于线性判别分析的改进算法,以较大幅度提高分类准确率。最后介绍了基于上述方法而设计实现的一种基于运动想象和运动启动电位的在线脑机接口系统。

稳态运动视觉诱发电位的诱发及在脑机接口中的应用进展

基于稳态运动视觉诱发电位(SSMVEP)的脑机接口(BCI)能够减少使用者的视觉疲劳,但其信号强度和系统性能仍不能代替基于稳态视觉诱发电位(SSVEP)的BCI。本文对提高SSMVEP信噪比的两种思路以及SSMVEP在BCI中的应用进行了综述。首先归纳和总结了影响SSMVEP诱导和性能的3个主要因素,即运动视觉刺激方式、运动视觉刺激参数和SSMVEP的信号处理方法;然后介绍了SSMVEP在医疗、娱乐等领域的应用;最后提出了目前使用SSMVEPBCI的限制以及未来发展方向。

基于脑-机接口的机械手实时运动控制系统研究

基于脑-机接口技术实现了一种机械手实时运动控制系统,该系统将稳态视觉诱发电位作为脑-机接口的输入信号,实现了脑电信号对机械手上升、下降、左转、右转、夹紧及松开6种动作的实时控制。系统利用6个LED发光二极管组成视觉刺激器,通过小波变换去除脑电背景噪声,并利用短时傅里叶变换进行诱发电位的频谱分析,将其转换为外部机械手的控制命令。实验结果表明,该系统能有效地控制机械手的实时运动。系统的实现为延伸和提高人类对外部设备的控制能力提供了一种新的途径。

脑控主被动协同刺激下肢康复训练系统研究与开发

目的对脑控主被动协同刺激康复训练关键技术进行研究,探索解决脑卒中患者在康复训练过程中主动参与程度较低的问题。方法探索一种全新的脑控主被动协同刺激康复训练方法,集成了脑机接口、稳态运动视觉诱发电位、虚拟现实和下肢康复训练机器人等技术,通过视觉刺激效果和被动训练作用于患者中枢神经,形成信息传递的闭环回路,实现运动神经通道的协同刺激,并搭建了脑控主被动协同刺激的下肢康复训练系统。结果所有被试者都在本系统的辅助下顺利完成了实验,检测程序在信息传输率为6.82～16.11bits/min时,系统检测的准确度为76.7%～96.7%,系统识别被试者运动意图的平均时间为6.01s,平均识别率为82.8%。结论本系统通过脑控主被动协同训练,提高了患者的训练效率和积极主动性,具有良好的应用前景。

基于视觉双特征的并行联合脑-机接口范式的研究

基于多特征的并行联合脑-机接口与单一特征脑-机接口相比,能利用更多信息和并行方式提高特征提取和系统执行效率。提出了一种基于稳态视觉诱发电位(SSVEP)和运动起始视觉诱发电位(MVEP)的双特征并行联合脑-机接口范式,通过设计3×3字符拼写范式,矩阵中纵列白色竖条按设定频率闪烁诱发SSVEP,横行中白色竖条随机运动诱发MVEP。实验表明,被试者关注目标字符时,两种特征脑电信号被同时诱发出来,并且对两种脑电信号进行特征识别能够检测出被试者选取的目标字符。联合范式并行的刺激编码方式有效节约了刺激诱发时间,为构建更为实用的联合脑-机接口提供了一种实现方法。

基于SSMVEP的脑-机接口视觉刺激探究

目的提出一种基于稳态运动视觉刺激诱发电位(SSMVEP)的方环运动刺激,并与目前常用的视觉刺激方式(牛顿环运动、方形闪烁和圆形闪烁)进行比较。方法选择9名受试者,分别采用四种刺激方式诱发脑电信号,采用典型相关分析(CCA)进行模式识别,并结合识别准确率与主观评分结果对刺激评价。结果方环运动诱发的脑电信号识别准确率(82.8%±14.1%)与牛顿环运动(83.3%±11.5%)相当,无显著性差异,但二者都低于方形闪烁(98.3%±4.1%)和圆形闪烁(99.2%±1.8%);图形形状对相同刺激模式(运动及闪烁)诱发的脑电信号识别准确率没有显著影响;方环运动的主观评价优于牛顿环运动、方形闪烁和圆形闪烁。结论方环运动刺激可以有效诱发脑电信号,并降低图形闪烁带来的不适感,可以作为视觉刺激应用于基于SSMVEP的BCI中。

基于EMD-ICA的视觉稳态诱发电位运动伪迹去除

脑电信号是大脑中产生的微弱电生理信号,经常会受到心电、眼电、运动伪迹的干扰。伪迹会使脑电信号产生较大的畸变。脑电信号与伪迹信号同属生理电信号。其中,运动伪迹的去除难度较高。采用经验模态分解结合独立成分分析的方法去除视觉稳态诱发电位信号中头部运动伪迹。对比单一经验模态分解与单一独立成分分析去噪算法,经验模态分解结合独立成分分析方法去噪算法效果最佳,去除运动伪迹的同时保留视觉稳态诱发电位固有信号。

A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm

Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.

A User-Friendly SSVEP-Based BCI Using Imperceptible Phase-Coded Flickers at 60Hz

A brain-computer interface(BCI)system based on steady-state visual evoked potentials(SSVEP)was developed by four-class phase-coded stimuli.SSVEPs elicited by flickers at 60Hz,which is higher than the critical fusion frequency(CFF),were compared with those at 15Hz and 30Hz.SSVEP components in electroencephalogram(EEG)were detected using task related component analysis(TRCA)method.Offline analysis with 17 subjects indicated that the highest information transfer rate(ITR)was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%.The online BCI system reached an averaged classification accuracy of 87.75±3.50%at 60Hz with 4s,resulting in an ITR of 16.73±1.63bpm.In particular,the maximum ITR for a subject was 80bpm with 0.5s at 60Hz.Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz,the results of the behavioral test indicated that,with no perception of flicker,the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz.Correlation analysis revealed that SSVEP with higher signal-to-noise ratio(SNR)corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance.This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.

深度线性判别分析用于两级脑控字符拼写解码

为了充分利用视听觉感知通道,实现高效的脑控字符拼写,提出一种基于区域的两级拼写范式。该范式的第一级基于运动视觉诱发电位进行目标区域选择,并引入码分多址方法进行区域编码,以提高其选择速率;第二级基于混合运动视觉诱发电位和听觉P300对目标字符进行编码,充分利用视听觉混合效应,改善目标字符选择的准确率。为了对采集的脑电信号进行有效的目标字符解码,提出一种结合深度线性判别分析的脑电信号分类识别算法。实验结果表明,深度线性判别分析算法在两级脑电信号的分类识别中平均分类准确率分别为61.7%和74%,明显高于传统方法和两种卷积神经网络方法的准确率。因此,该算法可有效地改善视听混合诱发两级脑机接口的指令解码性能。