scholarly journals Influence of layered skull modeling on the frequency sensitivity and target accuracy in simulations of transcranial current stimulation

2021 ◽  
Author(s):  
Heng Wang ◽  
Weiqian Sun ◽  
Jianxu Zhang ◽  
Zilong Yan ◽  
Chenyu Wang ◽  
...  
Keyword(s):  
Frequency Sensitivity

scholarly journals Individual motion perception parameters and motion sickness frequency sensitivity in fore-aft motion

2021 ◽  
Author(s):  
Tugrul Irmak ◽  
Ksander N. de Winkel ◽  
Daan M. Pool ◽  
Heinrich H. Bülthoff ◽  
Riender Happee
Keyword(s):  
Motion Sickness ◽  
Motion Perception ◽  
Time Constant ◽  
Storage Time ◽  
Velocity Storage ◽  
Strong Positive Correlation ◽  
Subjective Vertical ◽  
Time Constants ◽  
Frequency Sensitivity ◽  
Observer Model

AbstractPrevious literature suggests a relationship between individual characteristics of motion perception and the peak frequency of motion sickness sensitivity. Here, we used well-established paradigms to relate motion perception and motion sickness on an individual level. We recruited 23 participants to complete a two-part experiment. In the first part, we determined individual velocity storage time constants from perceived rotation in response to Earth Vertical Axis Rotation (EVAR) and subjective vertical time constants from perceived tilt in response to centrifugation. The cross-over frequency for resolution of the gravito-inertial ambiguity was derived from our data using the Multi Sensory Observer Model (MSOM). In the second part of the experiment, we determined individual motion sickness frequency responses. Participants were exposed to 30-minute sinusoidal fore-aft motions at frequencies of 0.15, 0.2, 0.3, 0.4 and 0.5 Hz, with a peak amplitude of 2 m/s2 in five separate sessions, approximately 1 week apart. Sickness responses were recorded using both the MIsery SCale (MISC) with 30 s intervals, and the Motion Sickness Assessment Questionnaire (MSAQ) at the end of the motion exposure. The average velocity storage and subjective vertical time constants were 17.2 s (STD = 6.8 s) and 9.2 s (STD = 7.17 s). The average cross-over frequency was 0.21 Hz (STD = 0.10 Hz). At the group level, there was no significant effect of frequency on motion sickness. However, considerable individual variability was observed in frequency sensitivities, with some participants being particularly sensitive to the lowest frequencies, whereas others were most sensitive to intermediate or higher frequencies. The frequency of peak sensitivity did not correlate with the velocity storage time constant (r = 0.32, p = 0.26) or the subjective vertical time constant (r = − 0.37, p = 0.29). Our prediction of a significant correlation between cross-over frequency and frequency sensitivity was not confirmed (r = 0.26, p = 0.44). However, we did observe a strong positive correlation between the subjective vertical time constant and general motion sickness sensitivity (r = 0.74, p = 0.0006). We conclude that frequency sensitivity is best considered a property unique to the individual. This has important consequences for existing models of motion sickness, which were fitted to group averaged sensitivities. The correlation between the subjective vertical time constant and motion sickness sensitivity supports the importance of verticality perception during exposure to translational sickness stimuli.

scholarly journals The Effects of Weighting Functions on the Performances of Robust Control Systems

Proceedings ◽  
2020 ◽  
Vol 63 (1) ◽  
pp. 46
Author(s):  
Mircea Dulau ◽  
Stelian-Emilian Oltean
Keyword(s):  
Robust Control ◽  
Control Systems ◽  
Tracking Error ◽  
Weighting Functions ◽  
Loop Control ◽  
Frequency Sensitivity ◽  
Sensitivity Functions ◽  
Loop Control System ◽  
Robust Control Design ◽  
Robust Control Systems

An important stage in robust control design is to define the desired performances of the closed loop control system using the models of the frequency sensitivity functions S. If the frequency sensitivity functions remain within the limits imposed by these models, the control performances are met. In terms of the sensitivity functions, the specifications include: shape of S over selected frequency ranges, peak magnitude of S, bandwidth frequency, and tracking error at selected frequencies. In this context, this paper presents a study of the effects of the specifications of the weighting functions on the performances of robust control systems.

Frequency Sensitivity in Nervous Systems

2001 ◽  
Vol 18 (2) ◽  
pp. 292-294 ◽  
Author(s):  
Liu Feng ◽  
Wang Wei
Keyword(s):  
Nervous Systems ◽  
Frequency Sensitivity

Developmental Changes in High-Frequency Sensitivity

1989 ◽  
Vol 28 (5) ◽  
pp. 241-249 ◽  
Author(s):  
Sandra E. Trehub ◽  
Bruce A. Schneider ◽  
Barbara A. Morrongiello ◽  
Leigh A. Thorpe
Keyword(s):  
High Frequency ◽  
Developmental Changes ◽  
Frequency Sensitivity

scholarly journals Exploring the dynamic mechanisms of steady-state visual evoked potentials at the whole-brain level: evidence from a computational study

2021 ◽  
Author(s):  
Ge Zhang ◽  
Yan Cui ◽  
Yangsong Zhang ◽  
Hefei Cao ◽  
Guanyu Zhou ◽  
...  
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Large Scale ◽  
Whole Brain ◽  
Frequency Sensitivity ◽  
Dynamic Mechanisms ◽  
Brain Level ◽  
The Brain ◽  
Visual Evoked

AbstractPeriodic visual stimulation can induce stable steady-state visual evoked potentials (SSVEPs) distributed in multiple brain regions and has potential applications in both neural engineering and cognitive neuroscience. However, the underlying dynamic mechanisms of SSVEPs at the whole-brain level are still not completely understood. Here, we addressed this issue by simulating the rich dynamics of SSVEPs with a large-scale brain model designed with constraints of neuroimaging data acquired from the human brain. By eliciting activity of the occipital areas using an external periodic stimulus, our model was capable of replicating both the spatial distributions and response features of SSVEPs that were observed in experiments. In particular, we confirmed that alpha-band (8-12 Hz) stimulation could evoke stronger SSVEP responses; this frequency sensitivity was due to nonlinear resonance and could be modulated by endogenous factors in the brain. Interestingly, the stimulus-evoked brain networks also exhibited significant superiority in topological properties near this frequency-sensitivity range, and stronger SSVEP responses were demonstrated to be supported by more efficient functional connectivity at the neural activity level. These findings not only provide insights into the mechanistic understanding of SSVEPs at the whole-brain level but also indicate a bright future for large-scale brain modeling in characterizing the complicated dynamics and functions of the brain.

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