scholarly journals Steady state evoked potential (SSEP) responses in the primary and secondary somatosensory cortices of anesthetized cats: nonlinearity characterized by harmonic and intermodulation frequencies

2020 ◽  
Author(s):  
Yota Kawashima ◽  
Rannee Li ◽  
Spencer Chin-Yu Chen ◽  
Richard Martin Vickery ◽  
John W. Morley ◽  
...  
Keyword(s):  
Steady State ◽  
Large Scale ◽  
Evoked Potential ◽  
Hierarchical Organization ◽  
Neural Population ◽  
Electrode Arrays ◽  
Computational Architecture ◽  
Somatosensory Cortices ◽  
Power Spectral ◽  
Nonlinear Computation

AbstractWhen presented with an oscillatory sensory input at a particular frequency, F [Hz], neural systems respond with the corresponding frequency, f [Hz], and its multiples. When the input includes two frequencies (F1 and F2) and they are nonlinearly integrated in the system, responses at intermodulation frequencies (i.e., n1*f1+n2*f2 [Hz], where n1 and n2 are non-zero integers) emerge. Utilizing these properties, the steady state evoked potential (SSEP) paradigm allows us to characterize linear and nonlinear neural computation performed in cortical neurocircuitry. Here, we analyzed the steady state evoked local field potentials (LFPs) recorded from the primary (S1) and secondary (S2) somatosensory cortex of anesthetized (ketamine-xylazine) cats while we presented slow (F1=23Hz) and fast (F2=200Hz) somatosensory vibration to the contralateral paw pads and digits. Over 9 experimental sessions, we recorded LFPs from N=1620 and N=1008 bipolar-referenced sites in S1 and S2 using electrode arrays. Power spectral analyses revealed strong responses at 1) the fundamental (f1, f2), 2) its harmonic, 3) the intermodulation frequencies, and 4) broadband frequencies (50-150Hz). To compare the computational architecture in S1 and S2, we employed simple computational modeling. Our modeling results necessitate nonlinear computation to explain SSEP in S2 more than S1. Combined with our current analysis of LFPs, our paradigm offers a rare opportunity to constrain the computational architecture of hierarchical organization of S1 and S2 and to reveal how a large-scale SSEP can emerge from local neural population activities.

scholarly journals Steady state evoked potential (SSEP) responses in the primary and secondary somatosensory cortices of anesthetized cats: Nonlinearity characterized by harmonic and intermodulation frequencies

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0240147
Author(s):  
Yota Kawashima ◽  
Rannee Li ◽  
Spencer Chin-Yu Chen ◽  
Richard Martin Vickery ◽  
John W. Morley ◽  
...  
Keyword(s):  
Steady State ◽  
Large Scale ◽  
Evoked Potential ◽  
Hierarchical Organization ◽  
Neural Population ◽  
Electrode Arrays ◽  
Computational Architecture ◽  
Somatosensory Cortices ◽  
Power Spectral ◽  
Nonlinear Computation

When presented with an oscillatory sensory input at a particular frequency, F [Hz], neural systems respond with the corresponding frequency, f [Hz], and its multiples. When the input includes two frequencies (F1 and F2) and they are nonlinearly integrated in the system, responses at intermodulation frequencies (i.e., n1*f1+n2*f2 [Hz], where n1 and n2 are non-zero integers) emerge. Utilizing these properties, the steady state evoked potential (SSEP) paradigm allows us to characterize linear and nonlinear neural computation performed in cortical neurocircuitry. Here, we analyzed the steady state evoked local field potentials (LFPs) recorded from the primary (S1) and secondary (S2) somatosensory cortex of anesthetized cats (maintained with alfaxalone) while we presented slow (F1 = 23Hz) and fast (F2 = 200Hz) somatosensory vibration to the contralateral paw pads and digits. Over 9 experimental sessions, we recorded LFPs from N = 1620 and N = 1008 bipolar-referenced sites in S1 and S2 using electrode arrays. Power spectral analyses revealed strong responses at 1) the fundamental (f1, f2), 2) its harmonic, 3) the intermodulation frequencies, and 4) broadband frequencies (50-150Hz). To compare the computational architecture in S1 and S2, we employed simple computational modeling. Our modeling results necessitate nonlinear computation to explain SSEP in S2 more than S1. Combined with our current analysis of LFPs, our paradigm offers a rare opportunity to constrain the computational architecture of hierarchical organization of S1 and S2 and to reveal how a large-scale SSEP can emerge from local neural population activities.

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.

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.

Cyclic Breathing Simulations in Large Scale Models of the Lung Airway From the Oronasal Opening to the Terminal Bronchioles

2012 ◽  
Author(s):  
D. Keith Walters ◽  
Greg W. Burgreen ◽  
Robert L. Hester ◽  
David S. Thompson ◽  
David M. Lavallee ◽  
...  
Keyword(s):  
Steady State ◽  
Boundary Conditions ◽  
Large Scale ◽  
Whole Body ◽  
Patient Specific ◽  
Cfd Simulations ◽  
Reduced Order ◽  
Scale Models ◽  
Steady State Simulation ◽  
Conducting Zone

Computational fluid dynamics (CFD) simulations were performed for unsteady periodic breathing conditions, using large-scale models of the human lung airway. The computational domain included fully coupled representations of the orotracheal region and large conducting zone up to generation four (G4) obtained from patient-specific CT data, and the small conducting zone (to G16) obtained from a stochastically generated airway tree with statistically realistic geometrical characteristics. A reduced-order geometry was used, in which several airway branches in each generation were truncated, and only select flow paths were retained to G16. The inlet and outlet flow boundaries corresponded to the oronasal opening (superior), the inlet/outlet planes in terminal bronchioles (distal), and the unresolved airway boundaries arising from the truncation procedure (intermediate). The cyclic flow was specified according to the predicted ventilation patterns for a healthy adult male at three different activity levels, supplied by the whole-body modeling software HumMod. The CFD simulations were performed using Ansys FLUENT. The mass flow distribution at the distal boundaries was prescribed using a previously documented methodology, in which the percentage of the total flow for each boundary was first determined from a steady-state simulation with an applied flow rate equal to the average during the inhalation phase of the breathing cycle. The distal pressure boundary conditions for the steady-state simulation were set using a stochastic coupling procedure to ensure physiologically realistic flow conditions. The results show that: 1) physiologically realistic flow is obtained in the model, in terms of cyclic mass conservation and approximately uniform pressure distribution in the distal airways; 2) the predicted alveolar pressure is in good agreement with previously documented values; and 3) the use of reduced-order geometry modeling allows accurate and efficient simulation of large-scale breathing lung flow, provided care is taken to use a physiologically realistic geometry and to properly address the unsteady boundary conditions.

The Research of Production Process Statistical Steady-State Based on the Hypothesis Test

2011 ◽  
Vol 314-316 ◽  
pp. 2433-2438
Author(s):  
Wei Zhi Wang
Keyword(s):  
Quality Control ◽  
Steady State ◽  
Production Process ◽  
Quantitative Method ◽  
Large Scale ◽  
Hypothesis Test ◽  
Influence Factors ◽  
Normal Operation ◽  
Scale Production ◽  
Large Scale Production

By only applying a after the event exam in the quality control of the batch production is not enough to meet the needs of modern large-scale production. To a certain extent, modern quality control is a dynamic process of the steady-state judge and adjustment. A simple and reliable steady-state judge rule and method is the premise to guarantee the normal operation. This paper provides a quantitative method to evaluate production process steady-state by analyzing influence factors based on mathematical statistics. The method is both suitable for simple production process and complex production process with sub-processes.

Sign in / Sign up

Export Citation Format

Share Document