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.
Attention can concentrate our mental resources on processing certain interesting objects, which is an important mental behavior and cognitive process. Recognizing attentional states have great significance in improving human’s performance and reducing errors. However, it still lacks a direct and standardized way to monitor a person’s attentional states. Based on the fact that visual attention can modulate the steady-state visual evoked potential (SSVEP), we designed a go/no-go experimental paradigm with 10 Hz steady state visual stimulation in background to investigate the separability of SSVEP features modulated by different visual attentional states. The experiment recorded the EEG signals of 15 postgraduate volunteers under high and low visual attentional states. High and low visual attentional states are determined by behavioral responses. We analyzed the differences of SSVEP signals between the high and low attentional levels, and applied classification algorithms to recognize such differences. Results showed that the discriminant canonical pattern matching (DCPM) algorithm performed better compared with the linear discrimination analysis (LDA) algorithm and the canonical correlation analysis (CCA) algorithm, which achieved up to 76% in accuracy. Our results show that the SSVEP features modulated by different visual attentional states are separable, which provides a new way to monitor visual attentional states.
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 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）
Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.
Motor imaging therapy is of great significance to the rehabilitation of patients with stroke or motor dysfunction, but there are few studies on lower limb motor imagination. When electrical stimulation is applied to the posterior tibial nerve of the ankle, the steady-state somatosensory evoked potentials (SSSEP) can be induced at the electrical stimulation frequency. In order to better realize the classification of lower extremity motor imagination, improve the classification effect, and enrich the instruction set of lower extremity motor imagination, this paper designs two experimental paradigms: Motor imaging (MI) paradigm and Hybrid paradigm. The Hybrid paradigm contains electrical stimulation assistance. Ten healthy college students were recruited to complete the unilateral movement imagination task of left and right foot in two paradigms. Through time-frequency analysis and classification accuracy analysis, it is found that compared with MI paradigm, Hybrid paradigm could get obvious SSSEP and ERD features. The average classification accuracy of subjects in the Hybrid paradigm was 78.61%, which was obviously higher than the MI paradigm. It proves that electrical stimulation has a positive role in promoting the classification training of lower limb motor imagination.
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）
Brain-computer interface (BCI) system is a system that achieves communication and control among humans and computers and other electronic equipment with the electroencephalogram (EEG) signals. This paper describes the working theory of the wireless smart home system based on the BCI technology. We started to get the steady-state visual evoked potential (SSVEP) using the single chip microcomputer and the visual stimulation which composed by LED lamp to stimulate human eyes. Then, through building the power spectral transformation on the LabVIEW platform, we processed timely those EEG signals under different frequency stimulation so as to transfer them to different instructions. Those instructions could be received by the wireless transceiver equipment to control the household appliances and to achieve the intelligent control towards the specified devices. The experimental results showed that the correct rate for the 10 subjects reached 100%, and the control time of average single device was 4 seconds, thus this design could totally achieve the original purpose of smart home 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%.
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.