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1.
稳态视觉诱发电位(SSVEP)是由持续的视觉刺激而诱发的节律性脑电信号。SSVEP频率由固定的视觉刺激频率及其谐波频率组成。二维集合经验模式分解(2D-EEMD)是经典的经验模式分解算法的改进算法,将分解拓展到二维方向上。本文首创性地将2D-EEMD应用于SSVEP。分解得到的本征模式函数(IMF)的二维图像可清晰地观测到SSVEP频率。经过噪声和伪迹滤除的SSVEP主要有效IMF成分投影到头图上,可以反映大脑对视觉刺激的时变趋势,以及大脑不同区域的反应程度,结果显示枕叶区对于视觉刺激的反应最为强烈。最后本文用短时傅里叶变换(STFT)对2D-EEMD的重构信号进行SSVEP频率提取,其识别准确率提高了16%。  相似文献   

2.
目的 基于稳态视觉诱发电位(steady-state visual evoked potential,SSVEP)和肌电(electromyography,EMG)的组合是广泛使用的混合BCI模式.而对于那些只能控制面部肌肉的使用者,咬合动作相关面部肌电通常与SSVEP结合使用.本研究探讨了下颌咬合相关肌电对枕部电极采集到的SSVEP的干扰情况,进而寻找即使在咬合动作下也可同时进行SSVEP识别的刺激频率.方法 根据咬合类型,实验分为3个模式组(无咬合、短咬和长咬合).在每组模式中,受试者同时注视3个闪烁在6.2 Hz、9.8 Hz和14.6 Hz视觉刺激目标.收集枕区4个位点的SSVEP后观察了在3种咬合模式下,各个闪烁刺激的SSVEP响应频谱,并利用典型相关分析方法识别了SSVEP信号,最后统计了准确率.结果 当刺激频率低于20 Hz时,即使有以上2种咬合动作,仍然可以避免其对SSVEP的干扰.根据这些信号的频域特征依然可以识别SSVEP.另外,在咬合动作下进行稳态视觉刺激时,SSVEP的识别率仍然很高(无咬合动作:100.0%,短咬:94.7%,长时间咬合:100.0%).结论 通过合理的频率选择和信号处理,即便下颌咬合动作和SSVEP刺激同时发生时,仍可将咬合动作对稳态视觉诱发电位的影响降低,而且达到较高的识别准确率.  相似文献   

3.
检验高刺激率诱发的稳态视觉诱发电位(SSVEP),可以被看作是低刺激率诱发的瞬态视觉诱发电位(tVEP)的线性叠加的科学假设。采用模式翻转视觉刺激,记录10名健康成年人在不同刺激率(4, 7.1, 7.7, 8.3, 9.1,10, 11.1, 12.5, 14.3, 16.7, 20, 25 rev/s)条件下的视觉诱发电位(VEP),然后用低刺激率(4 rev/s)诱发的tVEP及其经过幅值和相位调整后的波形,分别与刺激序列卷积合成对应高刺激率下的SSVEP,并采用Hotelling T2检验比较各tVEP模板条件下合成SSVEP与实际记录SSVEP波形之间的异同。结果显示,当使用常规记录的tVEP作为模板时,基于线性叠加原理合成的与实际记录的SSVEP在7.1~9.1 rev/s刺激率范围内不存在显著性差异(P>0.05),而在10~25 rev/s刺激率范围内存在统计学差异(P< 0.05),且两者之间误差随刺激率增加而增大;当对tVEP模板进行幅值和相位调整后,合成与实测SSVEP之间无统计学差异(P> 0.05),两者间的误差也显著下降,在被测刺激率范围内基本保持平稳。结果表明,不同刺激率下的瞬态诱发反应存在差异,稳态与瞬态诱发电位之间的线性叠加假设有赖于对各个刺激率下瞬态诱发电位的测定。  相似文献   

4.
目的稳态视觉诱发电位(steady-state visual evoked potential,SSVEP)是大脑对周期性视觉刺激产生的响应,已广泛应用于基于脑电(electroencephalogram,EEG)的脑-机接口(brain-computer interface,BCI)。SSVEP频率响应曲线通常是以发光二极管(light emitting diode,LED)作为视觉刺激器的方式获得的。近年来,计算机显示器广泛用于产生闪烁刺激,然而基于计算机显示器的SSVEP频率响应曲线少有研究。为此,本文研究了基于计算机显示器的SSVEP频率响应特性。方法利用采样正弦编码方法在普通LCD显示器上产生了42个刺激频率(频率范围4~45 Hz),并收集了10位健康受试者的脑电数据,以研究SSVEP幅值/信噪比(signal-to-noise ratio,SNR)与刺激频率的关系。结果较强SSVEP响应出现在大脑枕区。SSVEP基频幅值的峰值出现在10 Hz处,且第二峰值出现在20 Hz处。SSVEP二次谐波幅值的峰值出现在6 Hz且在高刺激频率处幅值较小。低、中频段的SSVEP基频信噪比处于相当的水平。结论本文的实验结果可以为基于计算机显示器的SSVEP-BCIs的频率选择提供依据。  相似文献   

5.
稳态视觉诱发电位(SSVEP)是大脑对外界光刺激的一种物理反应。本实验从光刺激的物理因素出发,研究不同灰度值刺激对SSVEP频率识别准确率的影响。本实验采用拉普拉斯融合进行空间滤波,并利用典型相关分析法进行频率识别。设置刺激界面背景为白色,改变刺激方块的灰度值,从而分析受试者的频率识别准确率。结果表明灰度值增加,频率识别准确率下降,灰度值为0时,频率识别准确率最高。  相似文献   

6.
目的 研究视觉感知空间分布对稳态视觉诱发电位(steady state visual evoked potential,SSVEP)调制成分的影响,为后续研究SSVEP调制成分与认知的关系奠定基础.方法 使用两种闪烁频率分别标记两个刺激图像,设计并实现了“完全重合”、“左右半分”和“对角四分”三种不同空间分布的脑电实验.共9名被试参与此次研究.本文计算并比较了3种实验条件下SSVEP基本成分与调制成分的信噪比空间分布,并做配对t检验.结果 调制成分的信噪比在“完全重合”时远大于“左右半分”和“对角四分”,且在大部分电极处都具有显著性差异.后两种分布下调制成分非常微弱,并且“对角四分”略高于“左右半分”.结论 当不同频率的刺激图像的视觉感知空间交叠时,SSVEP调制成分强,反之,则弱,且可能与交界区域有关.因此在有关SSVEP调制成分的研究中,需考虑不同频率标记的刺激图像的空间分布对其的影响.  相似文献   

7.
目的 采用弥散张量成像(DTI)和功能磁共振成像(fMRI)方法对垂体大腺瘤患者后视路的结构和功能进行评价,分析垂体大腺瘤对视放射和视觉皮层的影响.方法 采用1.5T磁共振扫描仪对23例垂体大腺瘤患者(患者组)及18名健康志愿者(对照组)进行组块设计的fMRI和DTI检查.fMRI刺激内容为全视野黑白翻转棋盘格,对照内容为黑色屏幕中心的白色"+",左右眼分别进行试验.采用SPM2进行fMRI数据后处理,通过组间分析方法分别获得左眼及右眼刺激下患者组与对照组间的激活差异图.DTI扫描采用13个弥散敏感梯度,b值为1000 s/mm2.采用Volume-one软件进行后处理,测量两侧视放射的FA值,分析两组间视放射FA值的差异.结果 在严格控制头动和机械噪声等影响因素后,最终各入组12例.患者组与对照组比较,初级视觉皮层激活范围及强度均明显缩小.且以对侧视觉皮层激活下降为主(P<0.05).患者组左侧及右侧视放射FA值均明显小于对照组(左侧:0.52±0.06比0.58±0.04,t=3.45,P<0.01;右侧:0.50±0.05比0.60±0.04,t=5.77,P<0.01).结论 垂体大腺瘤患者可发生后视路的微观结构改变和功能下降,而联合应用DTI和fMRI有助于早期了解这种脑结构与功能变化.  相似文献   

8.
目的通过高频刺激提升基于稳态视觉诱发电位(SSVEP)的脑-机接口(BCI)的用户舒适度,同时结合双频编码,克服高频导致的解码准确率下降问题。方法基于25.5~39.6 Hz频率设计了左右视野和棋盘格视觉刺激的2种60指令双频高频编码范式。共采集了13名受试者的数据,针对SSVEP信号进行频域空域特征分析,并根据频域诱发成分优化滤波器组参数。分别采用滤波器组的扩展典型相关分析(eCCA)、集成任务相关成分分析(eTRCA)以及任务判别模式分析(TDCA)等算法进行SSVEP识别以验证范式可行性。结果左右视野和棋盘格范式均成功诱发了稳定的SSVEP,左右视野基频及其谐波信噪比高,互调成分信噪比较弱,而棋盘格2个刺激频率的互调成分f1+f2的信噪比则明显高于30 Hz以上的二次谐波成分,同时还存在f2?f1成分和2f1?f2成分。结合脑地形图可以看出左右视野的f1和f2响应成分分别位于视野的对侧,而棋盘格则均集中于枕区中央。对于脑地形图振幅和信噪比的偏侧,左右视野刺激频率下PO3和PO4信噪比平均值符合对侧响应特征。5fb?1方法为最优滤波器组设置方法,左右视野TDCA的识别正确率最高,而棋盘格eTRCA和TDCA的识别正确率比较差异没有统计学意义(P>0.05),3种算法的信息传输速率均随数据长度的增加先升高后降低。结论设计的双频高频SSVEP-BCI范式能够较好平衡性能和舒适度,为实用性的大指令集BCI设计方法提供依据。  相似文献   

9.
脑-机接口(BCI)系统是通过脑电(EEG)信号实现人和计算机等设备之间的交流和控制的系统。本文阐述了基于BCI技术的无线智能家居系统的工作原理,利用单片机、LED灯组成视觉刺激器诱发得到稳态视觉诱发电位(SSVEP),再利用在LabVIEW平台上的功率谱变换方法实时处理不同频率刺激下产生的EEG信号,将其转化为不同的指令,由无线射频设备收发控制命令,实现家居设备的实时智能控制。实验结果表明,10名受试者的正确率均达到100%,单个设备的平均控制时间为4s,实现了家居设备的智能控制。  相似文献   

10.
当前脑-机接口(BCI)发展迅速,但高性能无创BCI往往需要借助显示设备诱发特定脑电信号,其中最常用的为计算机屏幕,因其难以实现可穿戴而限制BCI的便携性。将增强现实技术(AR)与BCI相结合形成AR-BCI可以解决这一问题,提升BCI的实用性。然而已有AR-BCI研究仅有少量报道,且识别正确率与速度均有待提升。通过采用微软的可穿戴增强现实设备Hololens作为显示设备,实现一种基于稳态视觉诱发电位(SSVEP)的AR-BCI,通过Hololens设备产生视觉刺激诱发八种频率的SSVEP信号,分别开展在线与离线实验,并与基于计算机屏幕的刺激进行比较。参与实验的12名被试成功在增强现实环境中诱发出明显的SSVEP信号,利用1和2 s长的EEG信号分别实现平均88.67%和98.6%的在线识别正确率。该研究表明,AR-BCI有望在日常生活中实现可穿戴的便携化高性能控制型BCI系统。  相似文献   

11.
The dependency of positive BOLD (PBOLD) and post-stimulus undershoot (PSU) on the temporal frequency of visual stimulation was investigated using stimulation frequencies between 1 and 44 Hz. The PBOLD peak at 8 Hz in primary visual cortex was in line with previous neuroimaging studies. In addition to the 8 Hz peak, secondary peaks were observed for stimulation frequencies at 16 and 24 Hz. These additional local peaks were contrary to earlier fMRI studies which reported either a decrease or a plateau for frequencies above 8 Hz but in line with electrophysiological results obtained in animal local field potential (LFP) measurements and human steady-state visual evoked potential (SSVEP) recordings. Our results also indicate that the dependency of PSU amplitude on stimulus frequency deviates from that of PBOLD. Although their amplitudes were correlated within the 1-13 Hz range, they changed independently at stimulation frequencies between 13 and 44 Hz. The different dependency profiles of PBOLD and PSU to stimulation frequency points to different underlying neurovascular mechanisms responsible for the generation of these BOLD transients with regard to their relation to inhibitory and excitatory neuronal activity.  相似文献   

12.
Multiple concurrently presented stimuli are thought to compete for neuronal processing resources. Such competitive stimulus interactions can be investigated by “frequency tagging” each stimulus with an individual temporal frequency. In this case, all stimuli will drive distinct steady-state visual evoked potentials (SSVEPs), hence allowing for an assessment of the distribution of processing resources. Here, we investigated whether competitive effects on SSVEP amplitudes are dependent upon the choice of tagging frequency of either the driving stimulus or a close-by competing stimulus. In particular, we were interested whether changes in amplitude are specific to a 10-Hz SSVEP, as it has been suggested that tagging frequencies within the alpha band drive uniquely characterized neural networks. If this was the case, an additional competition might be introduced when two stimuli are tagged with frequencies within the alpha band and thus compete for processing resources in similar networks. Additionally, we tested whether effects on SSVEP amplitude differ when the competing stimulus is tagged with a frequency of 12 Hz that produces a perceptible flicker when compared to an imperceptible 60-Hz flicker. We found a significant decrease in amplitude of 10- and 15-Hz SSVEPs upon presentation of the competing stimulus regardless of its tagging frequency. Our results clearly indicate that an SSVEP with a frequency within the alpha band and a 15-Hz SSVEP show similar sensitivity to effects of competition. Furthermore, the observed effects of competition on SSVEP amplitude occur independently of flicker perceptibility.  相似文献   

13.
After 40 years of investigation, steady-state visually evoked potentials (SSVEPs) have been shown to be useful for many paradigms in cognitive (visual attention, binocular rivalry, working memory, and brain rhythms) and clinical neuroscience (aging, neurodegenerative disorders, schizophrenia, ophthalmic pathologies, migraine, autism, depression, anxiety, stress, and epilepsy). Recently, in engineering, SSVEPs found a novel application for SSVEP-driven brain–computer interface (BCI) systems. Although some SSVEP properties are well documented, many questions are still hotly debated. We provide an overview of recent SSVEP studies in neuroscience (using implanted and scalp EEG, fMRI, or PET), with the perspective of modern theories about the visual pathway. We investigate the steady-state evoked activity, its properties, and the mechanisms behind SSVEP generation. Next, we describe the SSVEP-BCI paradigm and review recently developed SSVEP-based BCI systems. Lastly, we outline future research directions related to basic and applied aspects of SSVEPs.  相似文献   

14.
Steady-state visual evoked potentials (SSVEPs) reflect power changes at the stimulus driving frequency and have been used to assess brain activity reflecting cognitive processing. Only one study has demonstrated SSVEP modulation associated with working memory (WM), and none have compared the spatial localization of SSVEP modulations during WM performance with other brain imaging methods. Here we examined WM-related activity recorded with dense-array SSVEPs, analyzed using low resolution electromagnetic tomography, and compared the results to our previous findings using functional magnetic resonance imaging (fMRI). WM was associated with increased SSVEP activity over the right dorsolateral prefrontal cortex, paralleling our previous fMRI findings. Frontal WM-related SSVEP power correlated selectively with task performance. These results demonstrate the utility of SSVEPs for studying representational aspects of cognition.  相似文献   

15.
Flickering stimuli evoke an oscillatory brain response with the same frequency as the driving stimulus, the so-called steady-state visual evoked potential (SSVEP). SSVEPs are robust brain signals whose amplitudes are enhanced with attention and thus play a major role in the development and use of non-invasive Brain–Computer Interfaces (BCIs). We compared the modulation of SSVEP amplitudes when subjects directly gazed at a flickering array of static dots (overt attention) to when they covertly shifted attention to the dots keeping their eyes at central fixation. A discrimination task was performed at the attended location to ensure that subjects shifted attention as instructed. Horizontal eye movements (allowed in overt attention but to be avoided in covert attention) were monitored by the horizontal electrooculogram.  相似文献   

16.
Improvements in perceptual performance can be obtained when events in the environment are temporally predictable—and temporal predictability improves attention and sensory processing. The amplitude of the steady‐state visual evoked potential (SSVEP) has been shown to correlate with attention paid to a flickering stimulus even if the flickering stimulus is irrelevant for the task. However, to our knowledge, the validity of the SSVEP to study temporal attention has not been established. Therefore, we designed an SSVEP temporal attention task to evaluate whether the SSVEP and its temporal dynamics can be used to study temporal attention. We used a forced‐choice perceptual detection task while presenting task‐irrelevant visual flicker at alpha (10 Hz) and two surrounding frequencies (6 or 15 Hz). Temporal predictability was manipulated by having the interstimulus intervals (ISI) be constant or variable. Behavioral results replicated previous studies confirming the benefits of temporal expectations on performance for trials with constant ISI. EEG analyses revealed robust SSVEP amplitudes for all flicker frequencies, although a main effect of temporal expectations on SSVEP amplitude was not significant. Additional analyses revealed temporal predictability‐related modulations of SSVEP amplitude at 10 Hz and its second harmonic (20 Hz). The effect of temporal predictability was also observed for the 6 Hz flicker, but not for 15 Hz for any ISI condition. These results provide some evidence for the feasibility of the SSVEP technique to study temporal attention for stimuli with flicker frequencies around the alpha band.  相似文献   

17.
There have been many attempts to define eye dominance in normal subjects, but limited consensus exists, and relevant physiological data is scarce. In this study, we consider two different behavioral methods for assignment of eye dominance, and how well they predict fMRI signals evoked by monocular stimulation. Sighting eye dominance was assessed with two standard tests, the Porta Test, and a 'hole in hand' variation of the Miles Test. Acuity dominance was tested with a standard eye chart and with a computerized test of grating acuity. We found limited agreement between the sighting and acuity methods for assigning dominance in our individual subjects. We then compared the fMRI response generated by dominant eye stimulation to that generated by non-dominant eye, according to both methods, in 7 normal subjects. The stimulus consisted of a high contrast hemifield stimulus alternating with no stimulus in a blocked paradigm. In separate scans, we used standard techniques to label the borders of visual areas V1, V2, V3, VP, V4v, V3A, and MT. These regions of interest (ROIs) were used to analyze each visual area separately. We found that percent change in fMRI BOLD signal was stronger for the dominant eye as defined by the acuity method, and this effect was significant for areas located in the ventral occipital territory (V1v, V2v, VP, V4v). In contrast, assigning dominance based on sighting produced no significant interocular BOLD differences. We conclude that interocular BOLD differences in normal subjects exist, and may be predicted by acuity measures.  相似文献   

18.
Wu Z  Yao D 《Brain topography》2007,20(2):97-104
Previous studies suggested that there exists different neural networks for different frequency bands of steady-state visual evoked potential (SSVEP). What is the effect of the same cognitive task on different frequency SSVEPs? In this work, when a subject was conducting a graded memory task, a 8.3 or 20 Hz flicker was used as a background stimulation. The recorded EEGs were analyzed by the method of steady-state probe topography (SSPT), the results showed that SSVEPs under these two flicker conditions were similar to each other in the various stages of memory process, and were similar to the result of a high alpha band SSVEP as reported before. However, the SSVEP amplitude and latency in the lower frequency band is more clear and stable than that in the higher frequency band. These results suggest that the same cognitive task affects the different frequency SSVEP in a similar way, and the low frequency flicker is a better choice than the high frequency one in such as working memory study.  相似文献   

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