Scholarlink

Signal reconstruction is the process of recovering a continuous-time signal from a set of discrete samples taken from the given signal. In the real world, signals are often incomplete or corrupted due to noise, sensor failures, or transmission. In this project, we aim to reconstruct EEG signals using a wavelet transformation. The compressive sensing technique is used along with the orthogonal matching pursuit (OMP) algorithm, Bayesian refinement, adaptive thresholding techniques, and then we use inverse wavelet transformation to obtain the reconstructed signal. The precision of the signal that is reconstructed is conformed using mean squared error (MSE) and signal-to-noise ratio (SNR).

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Signal Reconstruction of the EEG Signal Using Python with the Help of Compressive Sensing and OMP

  • 会议论文

  • 发表日期：

    2026年

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  • Smitha Sasi,
  • P. Rajeswari,
  • C. P. Shreeramya,
  • M. Eshwari,
  • T. R. Bhavyashree,
  • M. S. Divya

摘要

Signal reconstruction is the process of recovering a continuous-time signal from a set of discrete samples taken from the given signal. In the real world, signals are often incomplete or corrupted due to noise, sensor failures, or transmission. In this project, we aim to reconstruct EEG signals using a wavelet transformation. The compressive sensing technique is used along with the orthogonal matching pursuit (OMP) algorithm, Bayesian refinement, adaptive thresholding techniques, and then we use inverse wavelet transformation to obtain the reconstructed signal. The precision of the signal that is reconstructed is conformed using mean squared error (MSE) and signal-to-noise ratio (SNR).