The maximum length sequence (m-sequence) has been successfully used to study the linear/nonlinear components of auditory evoked potential (AEP) with rapid stimulation. However, more study is needed to evaluate the effect of the m-sequence order in terms of the noise attenuation performance. This study aimed to address this issue using response-free electroencephalogram (EEG) and EEGs with nonlinear AEPs. We examined the noise attenuation ratios to evaluate the noise variation for the calculations of superimposed averaging and cross-correlation, respectively, which constitutes the main process in the deconvolution method using the dataset of spontaneous EEGs to simulate the cases of different orders (order 5 to 12) of m-sequences. And an experiment using m-sequences of order 7 and 9 was performed in true cases with substantial linear and nonlinear AEPs. The results demonstrate that the noise attenuation ratio is well agreed with the theoretical value derived from the properties of m-sequences on the random noise condition. The comparison of waveforms for AEP components from two m-sequences showed high similarity suggesting the insensitivity of AEP to the m-sequence order. This study provides a more comprehensive solution to the selection of m-sequences which will facilitate the feasible application on the nonlinear AEP with m-sequence method.
Steady-state flsash visual evoked potentials (SFVEPs) of 30 Hz were recorded for 46 normal subjects (89 eyes )and 35 patients (51 eyes )with optic neuropathy. The visual acuities of 58.8%affected eyes were less than 0.1. The recorded waveforms were analyzed by discrete Foruier transform (DTF). The amplitudes and phases of fundamental response component and second harmonic were abstracted as characteristic values of the waveform.The total abnormal ratio was 80. 4%. The abnormal types showed the reduced amplitudes,reduced amplitude with phase change, the phases changes, and flat wave. The advantages of SFVEPs in clinical application were discussed. （Chin J Ocul Fundus Dis,1994,10:213-215）
We have utilized the binocular flat and stereoscopic pattern to record visual evoked potentials (VEP) in normal and strabismic subjects. The aim was to find an electrophysiological correlation with the degree of binocular interaction in these subjects.The perception as tridimensional or flat derived from the disparity obtained with polaroid filters placed in front of the eyes. In normal subjects, the results demonstrated a significant increase of VEP amplitude during tridimensional perception of the pattern. In strabismic subjects the electrophysiological response were not correlated with the binocular conditions. The findings in the present study suggest that the binocular disparity in VEP examination is a useful technique and a better objective index for evaluating stereoscopic function than the psychophysical technique. （Chin J Ocul Fundus Dis,1992,8:10-13）
ObjectiveTo study the relationship between brain white matter fiber occult lesions and P100 wave latency of visual evoked potential (VEP) in neuromyelitis optica (NMO) patients by diffusion tensor imaging (DTI). MethodsTwenty patients with NMO who were treated between July 2008 and April 2009 were selected as the trial group. According to the VEP test, the latency of P100 wave was prolonged, the NMO patients were divided into VEP abnormal group (trial group 1) and VEP normal group (trial group 2). Twenty healthy adult volunteers served as the control group. The DTI examination in brain was done to measure the fractional anisotropy (FA) value of optic nerve (FAn), optic tract (FAt), and optic radiation (FAr);and the mean diffusivity (MD) value of optic nerve (MDn), optic tract (MDt), and optic radiation (MDr). The FA, MD, and P100 wave latency were compared between groups, and the correlation between MD, FA, and P100 wave latency of NMO were analyzed. ResultsIn the 20 NMO patients, 13 patients with VEP had prolonged bilateral P100 wave latency prolongation or no wave (trial group 1), and 7 patients had normal bilateral P100 wave latency (trial group 2). Compared with the trial group 2 and the control group, the FA values were significantly decreased, and the MD values were significantly increased in the trial group 1 (P<0.05). There was no significant difference in the FA and MD values between the trial group 2 and the control group (P>0.05). All FA (FAn, FAt, and FAr) values of each part of NMO patients were negatively correlated with the latency of P100 wave (P<0.05), all MD (MDn, MDt, and MDr) values were positively correlated with the latency of P100 wave (P<0.05). ConclusionDTI could show small pathylogical changes in the white matter fibers of visual pathway, and there is a correlation between DTI and VEP in NMO, suggesting that a more comprehensive assessment to the condition and prognosis can be made through the VEP in the clinical indicators.
Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.
The capacity of embryonic spinal cord tissue in the repair of injured structure of spinal cord has been noted for years. In order to investigate the embryonic spinal cord graft in the repair of motor function of injured spinal cord, the embryonic spinal cord tissue was transplanted to the hemisection cavity in spinal cord in adult rat. One hundred adult Wistar Rats were used to simulate the hemisectional injury of spinal cord by drilling 2-3 mm cavity in lumbar enlargement. Sixty rats were treated with rat embryonic spinal cord tissue grafting while the other forty were chosen as control. The outcome was evaluated according the combined behavioural score (CBS) and motor evoked potential (MEP) in the 1, 2, 4 and 12 weeks. The grafting group was superior to the control as assessed by CBS (P lt; 0.05), especially within 4 weeks. (P lt; 0.01). The restoration of the latent peak of early wave(P1, N1) was better in the grafting group, too. This suggested that embryonic spinal cord graft could improve the recovery of motor function of injured spinal cord in adult rat. The effect of the embryonic spinal cord tissue graft might be concerned with its secretion of several kinds of neurotrophic factors, nerve growth factor, nerve transmitted factor, or adjustment of hormone.
The impulsive electroencephalograph (EEG) noises in evoked potential (EP) signals is very strong, usually with a heavy tail and infinite variance characteristics like the acceleration noise impact, hypoxia and etc., as shown in other special tests. The noises can be described by α stable distribution model. In this paper, Wigner-Ville distribution (WVD) and pseudo Wigner-Ville distribution (PWVD) time-frequency distribution based on the fractional lower order moment are presented to be improved. We got fractional lower order WVD (FLO-WVD) and fractional lower order PWVD (FLO-PWVD) time-frequency distribution which could be suitable for α stable distribution process. We also proposed the fractional lower order spatial time-frequency distribution matrix (FLO-STFM) concept. Therefore, combining with time-frequency underdetermined blind source separation (TF-UBSS), we proposed a new fractional lower order spatial time-frequency underdetermined blind source separation (FLO-TF-UBSS) which can work in α stable distribution environment. We used the FLO-TF-UBSS algorithm to extract EPs. Simulations showed that the proposed method could effectively extract EPs in EEG noises, and the separated EPs and EEG signals based on FLO-TF-UBSS were almost the same as the original signal, but blind separation based on TF-UBSS had certain deviation. The correlation coefficient of the FLO-TF-UBSS algorithm was higher than the TF-UBSS algorithm when generalized signal-to-noise ratio (GSNR) changed from 10 dB to 30 dB and α varied from 1.06 to 1.94, and was approximately equal to 1. Hence, the proposed FLO-TF-UBSS method might be better than the TF-UBSS algorithm based on second order for extracting EP signal under an EEG noise environment.
Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a “third hand”, offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.
Brain computer interface is a control system between brain and outside devices by transforming electroencephalogram (EEG) signal. The brain computer interface system does not depend on the normal output pathways, such as peripheral nerve and muscle tissue, so it can provide a new way of the communication control for paralysis or nerve muscle damaged disabled persons. Steady state visual evoked potential (SSVEP) is one of non-invasive EEG signals, and it has been widely used in research in recent years. SSVEP is a kind of rhythmic brain activity simulated by continuous visual stimuli. SSVEP frequency is composed of a fixed visual stimulation frequency and its harmonic frequencies. The two-dimensional ensemble empirical mode decomposition (2D-EEMD) is an improved algorithm of the classical empirical mode decomposition (EMD) algorithm which extended the decomposition to two-dimensional direction. 2D-EEMD has been widely used in ocean hurricane, nuclear magnetic resonance imaging (MRI), Lena image and other related image processing fields. The present study shown in this paper initiatively applies 2D-EEMD to SSVEP. The decomposition, the 2-D picture of intrinsic mode function (IMF), can show the SSVEP frequency clearly. The SSVEP IMFs which had filtered noise and artifacts were mapped into the head picture to reflect the time changing trend of brain responding visual stimuli, and to reflect responding intension based on different brain regions. The results showed that the occipital region had the strongest response. Finally, this study used short-time Fourier transform (STFT) to detect SSVEP frequency of the 2D-EEMD reconstructed signal, and the accuracy rate increased by 16%.
This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects’ brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.