The steady-state visual evoked potential in vision research: A review.
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ABSTRACT:
SUBMITTER: Norcia AM
PROVIDER: S-EPMC4581566 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Publications
The steady-state visual evoked potential in vision research: A review.
Norcia Anthony M AM Appelbaum L Gregory LG Ales Justin M JM Cottereau Benoit R BR Rossion Bruno B
Journal of vision 20150101 6
Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials were first introduced over 80 years ago as a means to study visual sensation and perception. From the first single-channel recording of responses to modulated light to the present use of sophisticated digital displays composed of complex visual stimuli and high-density recording arrays, steady-state methods have been applied in a broad range of scientific and applied settings.The purpose of this artic ...[more]
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Project description:The steady-state motion visual evoked potential (SSMVEP) collected from the scalp suffers from strong noise and is contaminated by artifacts such as the electrooculogram (EOG) and the electromyogram (EMG). Spatial filtering methods can fuse the information of different brain regions, which is beneficial for the enhancement of the active components of the SSMVEP. Traditional spatial filtering methods fuse electroencephalogram (EEG) in the time domain. Based on the idea of image fusion, this study proposed an SSMVEP enhancement method based on time-frequency (T-F) image fusion. The purpose is to fuse SSMVEP in the T-F domain and improve the enhancement effect of the traditional spatial filtering method on SSMVEP active components. Firstly, two electrode signals were transformed from the time domain to the T-F domain via short-time Fourier transform (STFT). The transformed T-F signals can be regarded as T-F images. Then, two T-F images were decomposed via two-dimensional multiscale wavelet decomposition, and both the high-frequency coefficients and low-frequency coefficients of the wavelet were fused by specific fusion rules. The two images were fused into one image via two-dimensional wavelet reconstruction. The fused image was subjected to mean filtering, and finally, the fused time-domain signal was obtained by inverse STFT (ISTFT). The experimental results show that the proposed method has better enhancement effect on SSMVEP active components than the traditional spatial filtering methods. This study indicates that it is feasible to fuse SSMVEP in the T-F domain, which provides a new idea for SSMVEP analysis.
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Project description:Steady-state visual evoked potentials (ssVEPs) are commonly used for functional objective diagnostics. In general, the main response at the stimulation frequency is used. However, some studies reported the main response at the second harmonic of the stimulation frequency. The aim of our study was to analyze the influence of the stimulus design on the harmonic components of ssVEPs. We studied 22 subjects (8 males, mean age ± SD = 27 ± 4.8 years) using a circular layout (r 1 = 0-1.6°, r 2 = 1.6-3.5°, r 3 = 3.5-6.4°, r 4 = 6.4-10.9°, and r 5 = 10.9-18°). At a given eccentricity, the stimulus was presented according to a 7.5 Hz square wave with 50% duty cycle. To analyze the influence of the stimulus eccentricity, a background luminance of 30 cd/m2 was added to suppress foveal stray light effects; to analyze the influence of simultaneous foveal and peripheral stimulations, stimulations are performed without stray light suppression. For statistical analysis, medians M of the amplitude ratios for amplitudes at the second harmonic to the first harmonic and the probability of the occurrence of the main response at the second harmonic P(MCSH) are calculated. For stimulations with foveal stray light suppression, the medians were M 0 -1.6^ ∘ = 0.45, M 1.6 -3.5^ ∘ = 0.45, M 3.5 -6.4^ ∘ = 0.76, M 6.4 -10.9^ ∘ = 0.72, and M 10.9 -18^ ∘ = 0.48, and the probabilities were P 0-1.6^ ∘ (MCSH) = 0.05, P 1.6 -3.5^ ∘ (MCSH) = 0.05, P 3.5 -6.4^ ∘ (MCSH) = 0.32, P 6.4 -10.9^ ∘ (MCSH) = 0.29, and P 10.9 -18^ ∘ (MCSH) = 0.30. For stimulations without foveal stray light suppression, the medians M were M 0 -1.6^ ∘ = 0.29, M 1.6 -3.5^ ∘ = 0.37, M 3.5 -6.4^ ∘ = 0.98, M 6.4 -10.9 ^^ ∘ = 1.08, and M 10.9 -18^ ∘ = 1.24, and the probabilities were P 0-1.6^ ∘ (MCSH) = 0.09, P 1.6 -3.5^ ∘ (MCSH) = 0.05, P 3.5 -6.4^ ∘ (MCSH) = 0.50, P 6.4 -10.9^ ∘ (MCSH) = 0.55, and P 10.9 -18^ ∘ (MCSH) = 0.55. In conclusion, the stimulus design has an influence on the harmonic components of ssVEPs. An increase in stimulation eccentricity during extrafoveal stimulation leads to a transition of the main response to the second harmonic. The effect is enhanced by a simultaneous foveal stimulation.
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Project description:BACKGROUND:A steady-state visual-evoked potential (SSVEP) is a brain response to visual stimuli modulated at certain frequencies; it has been widely used in electroencephalography (EEG)-based brain-computer interface research. However, there are few published SSVEP datasets for brain-computer interface. In this study, we obtained a new SSVEP dataset based on measurements from 30 participants, performed on 2 days; our dataset complements existing SSVEP datasets: (i) multi-band SSVEP datasets are provided, and all 3 possible frequency bands (low, middle, and high) were used for SSVEP stimulation; (ii) multi-day datasets are included; and (iii) the EEG datasets include simultaneously obtained physiological measurements, such as respiration, electrocardiography, electromyography, and head motion (accelerator). FINDINGS:To validate our dataset, we estimated the spectral powers and classification performance for the EEG (SSVEP) datasets and created an example plot to visualize the physiological time-series data. Strong SSVEP responses were observed at stimulation frequencies, and the mean classification performance of the middle frequency band was significantly higher than the low- and high-frequency bands. Other physiological data also showed reasonable results. CONCLUSIONS:Our multi-band, multi-day SSVEP datasets can be used to optimize stimulation frequencies because they enable simultaneous investigation of the characteristics of the SSVEPs evoked in each of the 3 frequency bands, and solve session-to-session (day-to-day) transfer problems by enabling investigation of the non-stationarity of SSVEPs measured on different days. Additionally, auxiliary physiological data can be used to explore the relationship between SSVEP characteristics and physiological conditions, providing useful information for optimizing experimental paradigms to achieve high performance.
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