Digital Stochastic Measurement of a Nonstationary Signal With an Example of EEG Signal Measurement

Influence of Wilbraham-Gibbs Phenomenon on Digital Stochastic Measurement of EEG Signal Over an Interval

“…Simulations are performed with simulation model implemented in Matlab and VisualC and verified in previous extensive experimental verifications [16][17][18][19]25]. Measurement interval of 20 ms is chosen, because the developed instrument supports this interval (fundamental frequency of 50 Hz).…”

“…Papers [25,26] are the result of researching alternative solutions for such situations of a high-level ambient noise presence, and they describe the implementation of the DSM approach in measurement of non-stationary signal harmonics with varying measurement time. Measurement uncertainty in [25] is calculated by the developed theory while the EEG signal is selected as an example of real nonstationary signal.…”

“…Measurement uncertainty in [25] is calculated by the developed theory while the EEG signal is selected as an example of real nonstationary signal. Digital stochastic measurement of EEG signal harmonics is tested by simulations and by experiments in [25,26]. Tests are done both without adding noise and with adding noise (SNR varies from 10dB to -10 dB).…”

“…Following the previous research [25], this paper investigates the problem of influence of the WilbrahamGibbs phenomenon [27][28][29] on digital stochastic measurement of EEG signal in the time domain. The increase of measurement error is recognized as the consequence of the Wilbraham-Gibbs phenomenon.…”

Stochastic measurement of noise true RMS using two-bit flash A/D converters

“…The measurement uncertainty is estimated using the applied theory of discrete signals and systems, underpinned by algebraic calculations [3]. An alternative strategy, the "stochastic measurement over an interval" [4], has been researched in three challenging areas: measurements that require high accuracy and linearity [5][6][7], measurement of fast-changing signals and noisy signals [8,9]. Numerous simulations, prototype instruments and experiments have proven the metrological applicability of the stochastic approach [4][5][6][7][8][9].…”