Characteristic frequency detection of steady-state visual evoked potentials based on filter bank second-order underdamped tristable stochastic resonance

Shi, Peiming; Li, Jinxia; Zhang, Wenyue; Li, Mengdi; Han, Dongying

doi:10.1016/j.bspc.2023.104817

Cited by 6 publications

(2 citation statements)

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“…In equation (25), h represents the time step used in the RK4 algorithm for numerical calculations. The variables a, b, c, and r denote the parameters of the LSBSR system.…”

Section: Signal Processing Strategy Of the Lsbsr Systemmentioning

confidence: 99%

“…The linear response theory is then developed by Gammaitoni et al [13], and the theory of residence time distribution is later introduced by Zhou and Moss [14]. By then, the three major theories which lay the classical theoretical framework of SR systems are formally proposed, and they are successfully applied in the fields of mechanical fault diagnosis [15][16][17][18], electromagnetism and communication [19][20][21], image processing [22][23][24] and medical signal analyses [25][26][27][28], etc.…”

Section: Introductionmentioning

confidence: 99%

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Linearly-coupled sigmoid bistable stochastic resonance for weak signal detection

Zong,

An,

Zhang

et al. 2024

Meas. Sci. Technol.

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The paper focuses on developing a stochastic resonance system designed for the detection of weak signals under Alpha-stable-distributed noises. Initially, in view of the strong impulsive characteristics of noises, a Linearly-coupled Sigmoid Bistable Stochastic Resonance (LSBSR) system is proposed, which is constructed by potential function and sigmoid function. Through formula derivation, it is theoretically proved that the output SNR of the LSBSR system is superior to that of the classical bistable stochastic resonance (CBSR) system. Then, a new signal processing strategy based on the LSBSR system is introduced. Simulation experiments have demonstrated that under the input SNR=-20dB, the detection probability of the LSBSR system exceeds 95% for the Alpha-stable-distributed noise with α=1.5. When α is reduced to 0.1, the detection probability approaches 80%, significantly outperforming other detection methods. Finally, the LSBSR system is applied to detect sea-trial signals with an SNR improvement (SNRI) of 22.5dB, which further validates the practicability of the proposed system.

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“…In equation (25), h represents the time step used in the RK4 algorithm for numerical calculations. The variables a, b, c, and r denote the parameters of the LSBSR system.…”

Section: Signal Processing Strategy Of the Lsbsr Systemmentioning

confidence: 99%