white gaussian noise sampling function wgn  (MathWorks Inc)

Journal: Scientific Reports

Article Title: Measurement of temporal and spatial parameters of ice hockey skating using a wearable system

doi: 10.1038/s41598-022-26777-9

Figure Lengend Snippet: The skater's velocity and trajectory are obtained by time integration and double-time integration of the free acceleration obtained by the IMUs. Methods were originally proposed (S1–S4) to remove the drift in these integration processes.

Article Snippet: S2 , (i) Similar to S1.i (ii) Generate white Gaussian noise using a MATLAB wgn function (input arguments: the signal-to-noise ratio: − 15 decibels (dB), and its length equal to the sensor acceleration time series). Then scale it to match the corrected sensor acceleration amplitude range in the selected resting period. Afterward, subtract the obtained output from the corrected sensor acceleration time series and store it as corrected acceleration time series (iii) Calculate the corrected free acceleration (during motion) using the corrected acceleration time series (output of S2.ii) and the sensor orientation (iv) Similar to S1.iii.

Techniques:

Journal: Nature Communications

Article Title: Fluctuations in instantaneous frequency predict alpha amplitude during visual perception

doi: 10.1038/s41467-017-02176-x

Figure Lengend Snippet: Hypothesis and task design. a A simulated example of an alpha oscillation that is both increasing in frequency and decreasing in amplitude over time, as exemplified in the left and right plots underneath, respectively. Verticle lines indicate evenly spaced time bins matching one cycle of the initial oscillatory frequency. Plotted below the amplitude and frequency traces are hypothetical raster plots corresponding with periods of efficient visual information processing according to the desychonization and instantaneous frequency hypotheses, respectively. b Amplitude spectrum from a representative subject. Note the general 1/f distribution of amplitude over frequency, and the pronounced bump in the alpha range. The circular outline indicates peak alpha frequency, whereas gray dots indicate hypothetical shifts away from the peak alpha frequency over the course of the trials outlined in c . c Along with the same example trial in b , now termed a correct trial, we have plotted a hypothetical incorrect trial that decreases in frequency and amplitude with magnitudes corresponding with the spectrum in c . Note that on the left side of the panel, the two traces are in phase, but become out of phase over the course of the trial, meaning frequency shifts could lead to offsets in phase through a relative speeding or slowing of the underlying signals. In addition to phase offsets, shifts in frequency away from peak alpha could also impact alpha amplitude as shown in the bottom right panel. d Task Design. The target was a Gaussian—windowed Gabor (mean contrast = 5%) presented for 8.3 ms. The target was immediately preceded and followed by one frame (~8.3 ms each) of gaussian—windowed white noise. Between target presentations, subjects passively fixated at the center of a gray screen for 3000–4000 ms (uniform distribution of ITIs). Target location (centered 8.5° left or right from fixation) was randomly selected with the only constraint that an equal number of trials were presented on both sides of fixation

Article Snippet: To generate white noise look-up-tables, we used the built in white Gaussian noise (wgn) function in Matlab with parameter output power set to 1 dBw.

Techniques: