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.

基于SSVEP信号的相频特性分类算法研究

目前基于稳态视觉诱发电位(SSVEP)的脑-机接口在人机协作中受到广泛关注,现有面向SSVEP信号的相位与频率信息的深度学习分类方法,仍存在由于信息利用不充分导致的SSVEP信号分类效果较差等问题。而目前已出现多种分类算法用于解决上述问题。本文基于迁移学习思想提出一种用于SSVEP信号分类的深度神经网络模型,将快速傅里叶变换后的复向量作为输入,对各个导联的实、虚部向量进行卷积,学习对应的相频特性。该模型分为两部分:第一部分利用所有被试者之间的统计共性获得相位和频率信息的全局相频特征模块;第二部分利用训练好的全局相频特征模块对局部相频特征模块进行初始化,通过局部相频特征模块的进一步强化学习对训练参数进行微调,以减少每个被试者之间的个体差异。在公开数据集BETA上进行测试,在时窗长度为1.5 s时,平均准确率和平均信息传输率分别为89.98%和71.80 bit/min。实验结果表明,与其他方法相比,本文的分类算法模型取得了较为不错的分类效果,所设计的全局、局部相频特征模块能够改善个体差异因素对分类结果的影响,为深入挖掘、利用SSVEP信号中的相位和频率信息提供了全新思路。

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.

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.

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.

基于改进扩展典型相关分析的SSVEP信号识别方法

现有的稳态视觉诱发电位(SSVEP)的信号识别方法没有充分关注信号的相位特征在识别过程中的重要作用,为此提出一种扩展典型相关分析(eCCA)的改进方法。将联合频率-相位调制编码的刺激范式中的相位参数添加到由受试者训练数据所构造的参考信号,以此来实现对eCCA的相位约束,从而提升eCCA方法对SSVEP信号的识别性能。通过在公开数据集上与现有的SSVEP信号识别方法进行对比实验,表明所提方法对SSVEP信号的平均识别率提高到82.76%,信息传输速率提高至116.18 bits/min,且具有更好的稳定性。

基于时频多尺度的SSVEP信号快速识别方法

目前基于稳态视觉诱发电位(SSVEP)的脑机接口在人机协作中受到广泛关注,但较短时长SSVEP信号仍面临信噪比较低、特征提取不充分的问题。该文从频域、时域以及空域3个角度分析并提取SSVEP信号特征。首先该方法从由频域实部信息和虚部信息整合的3维重校正特征矩阵中提取幅值和相位特征信息。然后在时域中通过训练多个刺激时窗尺度的样本增强模型表征能力。最后利用不同尺度的1维卷积核,并行提取通道空间和频域上的多尺度特征信息。该文在两种不同的视觉刺激频率和频率间隔的公开数据集上进行实验,在时窗为1 s时的平均准确率和平均信息传输率(ITR)均优于现有的其他方法。

基于PSD特征的FBCCA脑电信号识别方法

当前基于稳态视觉诱发电位(steady-state visual evoked potential,SSVEP)的脑机接口(brain-computer interfaces,BCIs)使用的都是单一识别算法,针对不同时间长度的识别准确率较低。提出了一种基于滤波器组的典型相关分析(filter bank canonical correlation analysis,FBCCA)与功率谱密度(power spectral density,PSD)分析相结合的SSVEP识别算法,可以提高SSVEP识别的普适性与准确率。该方法使用FBCCA寻找高相似度的参考频率信号,再通过多组PSD分析来锁定最终的响应频率,完成频率识别。该方法无需经过训练就能得到较高的识别准确率。实验结果表明:在刺激时长为1 s时,该方法能达到86.61%的准确率,比PSD分析方法提升了5.44%,比典型相关性分析方法(canonical correlation analysis,CCA)提升了10.38%的准确率,比FBCCA提升了8.86%的准确率。

一种基于SSVEP的仿人机器人异步脑机接口控制系统

设计了稳态视觉诱发电位(SSVEP)空闲状态检测的特征提取方法,建立了基于SSVEP的异步脑机接口二级分类器结构,开发了基于TI CC2430芯片的无线传感器网络模块,实现了机器人控制命令的远程传送,使该仿人机器人系统具有脑电控制、语音交互、游戏手柄交互、机器视觉与避障等功能.通过SSVEP空闲状态检测实验验证了脑机接口系统异步控制的有效性.

基于SSVEP直接脑控机器人方向和速度研究

直接用思维意图来控制机器人而没有大脑外周神经和肌肉的参与是人类的一个梦想,目前这一研究已成为国际前沿热点和突破点.传统的脑控机器人(Brain-controlled robot,BCR)主要控制其方向,而本文旨在探讨能够同时脑控机器人方向和速度的有效方法.采用可分类目标数多、单次识别率高且训练时间短的稳态视觉诱发电位(Steady state visual evoked potentials,SSVEP)脑机交互(Brain-computer/machine interaction,BCI/BMI)方法,为脑控机器人运动规划了向左、向右、前进和后退4个方向,设计了低速、中速和高速3级运动速度并组合了9个脑控指令;进而比较并优化了SSVEP刺激目标布局间距以及刺激目标闪烁时间,采用典型相关分析(Canonical correlation analysis,CCA)进行识别.结果表明恰当设置SSVEP刺激目标数及其布局间距和刺激目标闪烁时间,可以有效提高被试/用户直接脑控机器人的性能;优化的SSVEP刺激范式三结合适应SSVEP解码的典型相关分析,8名被试脑控机器人到达终点平均用时为2分40秒,最少用时1分29秒;同时,在脑控机器人运动过程中触碰障碍平均次数为0.88,最少碰触次数为0.本研究显示基于SSVEP的脑机交互可以作为直接脑控机器人灵活运动的一种可选方法,能够实现对机器人多个运动方向和多级速度的控制;也证实了适当增加刺激目标间距可以有效提高SSVEP-BCI脑控指令识别的正确率,说明了该脑控方法的性能与刺激被试的范式有关;再次验证了CCA算法在基于SSVEP的脑机交互中具有优良的效果.最后,为克服单一SSVEP范式存在的局限,本研究也尝试把该范式与运动想象相结合的混合范式用于脑控机器人方向和速度,并进行了初步的研究,表明可以进一步改善控制速度和提高被试舒适度.本文可望为基于SSVEP或与运动想象混合的脑机交互应用于分级或精细控制机器人方向和速度提供思路,并为直接脑控机器人技术推向实际应用打下一定的基础.

RSVP与SSVEP混合脑电信号刺激与多类事件检测

提出一种新的基于快速序列视觉呈现(Rapid Serial Visual Presentation,RSVP)与稳态视觉诱发电位(Steady-State Visually Evoked Potential,SSVEP)组合范式的脑电信号(Electroencephalogram,EEG)刺激与多类事件检测方法。对诱发的原始脑电信号通过电位重参考、基线去除、空间滤波等预处理操作去除数据的伪迹和噪声,通过自举聚合决策树(Bagging Tree,BT)和支持向量机(Supported Vector Machine,SVM)等机器学习算法,对14名受试者双重刺激诱发的脑电信号进行目标与频率相结合的多类事件检测,通过实验验证了该组合范式诱发的脑电信号具有良好的多类可分性,为开发基于RSVP和SSVEP两种范式的混合型脑-机接口应用提供了一种新的有效途径。同时,实验结果还表明,基于机器学习的BT和SVM模型对RSVP和SSVEP组合范式诱发的EEG信号进行多类识别的性能明显优于传统的典型关联分析(Canonical Correlation Analysis,CCA)算法的性能。

基于SSVEP的脑控小车系统的研究

文中设计了一种基于SSVEP的脑控小车分级速度和方向控制的系统,存在向左、向右、向后、向前(一级、二级、三级)6个脑控命令。在刺激范式、刺激时间、空间布局三方面进行了优化,实验表明扩大刺激目标间距、加强刺激时间能够提高目标识别准确率。在脑电解码方面,采用HHT(Hibert-Huang Transform)和CCA(Canonical Correlation Analysis)对比方式,10名被试参与此次研究,结果表明HHT解码方式比CCA在准确度方面提高了6.59%;在特征分类方面,采用支持向量机(Support vector machine,SVM)形式。该方向和分级速度控制系统实现了小车在速度和方向上的灵活控制,优化方法提高脑了控小车的准确度与实时性。实验结果显示在选取范式3,刺激时间3s的条件下,10名被试平均识别准确率高达92.50%。文中理论可望为脑控设备走出实验室打下坚实基础。

基于P300⁃SSVEP的双人协同脑⁃控机械臂汉字书写系统

基于脑-机接口(Brain-computer interface,BCI)的脑-控技术发展迅速,取得较大进展。然而,现有研究多采用单人脑控方式,存在执行效率低、可控自由度低的问题,难以满足复杂条件下的操控任务需求。针对此问题,本文采用时-频-相混合编码的视图脑-机交互方法,设计双人协同策略,通过解码P300和稳态视觉诱发电位(Steady-state visual evoked potential,SSVEP)脑电特征,开发了108指令的双人协同脑-控机械臂系统,实现双人同时对汉字一笔一划的书写。8名被试在线平均正确率为87.92%,平均在线信息传输速率(Information-transfer rate,ITR)为66.00 b/min。该系统扩展了BCI信息交互方式,初步验证了协同BCI操控机械臂的可行性和有效性,为协同BCI提供了技术支撑。

SSVEP-BCI抗自由眨眼稳定性的ANFIS方法

针对伪迹干扰下脑机接口稳定性问题,以自由眨眼动作下稳态视觉诱发脑机接口的稳定性为切入点,进行了稳态视觉诱发脑电信号去眼电伪迹(electroculography,简称EOG)研究。提出了一种基于自适应神经模糊推理系统(adaptive neuro-fuzzy inferency system,简称ANFIS)的无眼电电极下脑电信号眼电伪迹的自适应消除方法并进行实验,验证该方法对自由眨眼动作下稳态视觉诱发脑机接口稳定性的提高。该伪迹消除方法通过自适应神经模糊推理系统逼近眼电信号源至眼电伪迹的非线性变换函数,达到消除脑电信号中眼电伪迹的目的。算法通过前额叶区脑电信号获得替代性眼电信号源,经延时处理后,输入自适应噪声消除器中以消除各通道脑电信号中的眼电伪迹。通过自由眨眼动作下稳态视觉刺激实验,对该伪迹消除方法中各参数及函数的选择进行了研究,并将该方法与经典滤波和传统独立成分分析(independent component analysis,简称ICA)进行对比,证明了该方法在消除眼电伪迹的情况下保留了稳态视觉刺激的有效信息,识别正确率较经典滤波相比最高提高了6.25%,较传统ICA相比最高提高10%,保证了稳态视觉诱发脑机接口在自由眨眼动作下的稳定性。

整合贝叶斯动态停止策略对SSVEP-BCIs的性能提升研究

由于大脑的状态处于不断变化中,因此提取自脑电图中的特征,其质量并不总是足够高以保证脑-机接口（BCI）的可靠输出。提出了基于贝叶斯估计的动态停止（DS）策略,并将其整合到基于稳态视觉诱发电位（SSVEP）的BCI系统中,以进一步优化和提升SSVEP-BCIs的性能。10人次的实验结果表明,相比于传统的静态停止（FS）策略,DS策略能有效提升信息传输率（ITR）,尤其是使用扩展的典型相关分析的DS策略相比FS策略提升了7.85%。另外,使用总体任务相关成分分析的DS策略得到的平均和最高ITR分别是352.3和435.7 bits/min。因此,证明了通过整合DS策略可以进一步提升SSVEP-BCIs的性能,并有希望推广到实际应用。

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.

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.