Time-frequency analysis of red-green visual flickers based on steady-state visual evoked potential recording

2016 ◽  
Author(s):  
Panisa Dechwechprasit ◽  
Montri Phothisonothai ◽  
Suchada Tantisatirapong
Keyword(s):  
Steady State ◽  
Frequency Analysis ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Potential Recording ◽  
Visual Evoked

Olfactory Modulation of Steady- State Visual Evoked Potential Topography in Comparison with Differences in Odor Sensitivity

2002 ◽  
Vol 16 (2) ◽  
pp. 71-81 ◽  
Author(s):  
Caroline M. Owen ◽  
John Patterson ◽  
Richard B. Silberstein
Keyword(s):  
Steady State ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Behavioral Responses ◽  
Simultaneous Recording ◽  
Odor Concentration ◽  
Odor Detection ◽  
Detection Response ◽  
Response Groups ◽  
Visual Evoked

Summary Research was undertaken to determine whether olfactory stimulation can alter steady-state visual evoked potential (SSVEP) topography. Odor-air and air-only stimuli were used to determine whether the SSVEP would be altered when odor was present. Comparisons were also made of the topographic activation associated with air and odor stimulation, with the view toward determining whether the revealed topographic activity would differentiate levels of olfactory sensitivity by clearly identifying supra- and subthreshold odor responses. Using a continuous respiration olfactometer (CRO) to precisely deliver an odor or air stimulus synchronously with the natural respiration, air or odor (n-butanol) was randomly delivered into the inspiratory airstream during the simultaneous recording of SSVEPs and subjective behavioral responses. Subjects were placed in groups based on subjective odor detection response: “yes” and “no” detection groups. In comparison to air, SSVEP topography revealed cortical changes in response to odor stimulation for both response groups, with topographic changes evident for those unable to perceive the odor, showing the presence of a subconscious physiological odor detection response. Differences in regional SSVEP topography were shown for those who reported smelling the odor compared with those who remained unaware of the odor. These changes revealed olfactory modulation of SSVEP topography related to odor awareness and sensitivity and therefore odor concentration relative to thresholds.

Forced-Choice Preferential Looking and Visual Evoked Potential Acuities of Visually Impaired Children

1992 ◽  
Vol 86 (1) ◽  
pp. 21-24 ◽  
Author(s):  
M.C. Bane ◽  
E.E. Birch
Keyword(s):  
Steady State ◽  
Success Rate ◽  
Visually Impaired ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Forced Choice ◽  
Preferential Looking ◽  
Impaired Children ◽  
Visually Impaired Children ◽  
Visual Evoked

In the authors’ previous study, the success rate for forced-choice preferential looking (FPL) with preverbal visually impaired children was higher than that with pattern visual evoked potential (VEP). The current study sought to increase the VEP success rate and to improve agreement between the FPL and the VEP acuity estimates using horizontal-bar stimuli for children with nystagmus and steady-state presentation for those without nystagmus.

Frequency decomposition and phase synchronization of the visual evoked potential using the empirical mode decomposition

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Byuckjin Lee ◽  
Byeongnam Kim ◽  
Sun K. Yoo
Keyword(s):  
Empirical Mode Decomposition ◽  
Brain Function ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Posterior Lobe ◽  
Time Frequency ◽  
Brain Functions ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Visual Evoked

AbstractObjectivesThe phase characteristics of the representative frequency components of the Electroencephalogram (EEG) can be a means of understanding the brain functions of human senses and perception. In this paper, we found out that visual evoked potential (VEP) is composed of the dominant multi-band component signals of the EEG through the experiment.MethodsWe analyzed the characteristics of VEP based on the theory that brain evoked potentials can be decomposed into phase synchronized signals. In order to decompose the EEG signal into across each frequency component signals, we extracted the signals in the time-frequency domain with high resolution using the empirical mode decomposition method. We applied the Hilbert transform (HT) to extract the signal and synthesized it into a frequency band signal representing VEP components. VEP could be decomposed into phase synchronized δ, θ, α, and β frequency signals. We investigated the features of visual brain function by analyzing the amplitude and latency of the decomposed signals in phase synchronized with the VEP and the phase-locking value (PLV) between brain regions.ResultsIn response to visual stimulation, PLV values were higher in the posterior lobe region than in the anterior lobe. In the occipital region, the PLV value of theta band was observed high.ConclusionsThe VEP signals decomposed into constituent frequency components through phase analysis can be used as a method of analyzing the relationship between activated signals and brain function related to visual stimuli.

scholarly journals Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5309
Author(s):  
Akira Ikeda ◽  
Yoshikazu Washizawa
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Extraction Methods ◽  
Stimulation Frequency ◽  
Event Related Potential ◽  
Spectrum Estimation ◽  
Complex Valued ◽  
Visual Evoked

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

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