Novel Elimination Method of Baseline Drift Based on Improved Least Square Method

EasyChair Preprint 7863

Abstract

With the development of brain computer interface (BCI),the application of bioelectrical signals in the field of intelligent medical devices has been flourishing. Currently, how to remove the interference in the electrical signal and improve the recognition accuracy has attracted great attention. In the process of electromyographic (EMG) signal acquisition, baseline drift is a serious issue, which can affect the signal recognition accuracy, the traditional least square method (LSM) cannot remove the filtered baseline drift component within the window. To address this issue efficiently, a modified least method is designed in this paper, which employs a polynomial fit to remove the baseline drift component within the window by the curvature of the polynomial. The designed method can not only retain the advantages of the LSM in terms of small operation size, but also improve the baseline drift removal capability, providing a solution for a high-precision embedded bioelectric signal acquisition device. Experimental results show that the improved least square method (ILSM) improves the baseline drift removal capability by about 5% over the LSM. In addition; In addition, compared to LSM, the ILSM can reduced the number of window openings.

Keyphrases: Electromyography, baseline drift, improved least square method, wavelet decomposition

@booklet{EasyChair:7863,
  author    = {Ruhao Zhang and Xin Xu and Yin Zhang and Tingting Xu},
  title     = {Novel Elimination Method of Baseline Drift Based on Improved Least Square Method},
  howpublished = {EasyChair Preprint 7863},
  year      = {EasyChair, 2022}}