A Method for Blink Artifact Detection and Removal with Wavelet Transform and Hilbert Transform
- DOI
- 10.2991/bep-16.2017.22How to use a DOI?
- Keywords
- EEG; blink artifact; wavelet transform; Lipschitz index; Hilbert transform; blink zone
- Abstract
This paper presents a method of blink artifact removal of single lead EEG signal. After a wavelet transform signal singularity points are detected and modulus maxima values of wavelet transform are applied to find peaks of blink artifacts. By using Hilbert transform instantaneous frequency is obtained. With the peaks as clues instantaneous frequencies are used to identify the zones appearing blink artifacts. With a stationary wavelet transform, EEG signal is decomposed to the depth on which the frequency bandwidth of approximation coefficients covers the frequency bandwidth of blink artifacts. The approximation coefficients in a blink artifact zone are estimated by the fitting data using the data outside of the blink zone. To the detail coefficients de-noising threshold value in a blink zone is estimated with the threshold values of two sides out of the blink zone. After these coefficients are corrected, the signal is reconstructed to get the EEG signal with blink artifact removal. The experimental results show that the proposed method is effective to detect and remove blink artifacts and avoid affecting the EEG signal outside the blink artifact zones.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiaobai CAI AU - Junjun CHEN PY - 2016/12 DA - 2016/12 TI - A Method for Blink Artifact Detection and Removal with Wavelet Transform and Hilbert Transform BT - Proceedings of the 2016 International Conference on Biological Engineering and Pharmacy (BEP 2016) PB - Atlantis Press SP - 104 EP - 109 SN - 2468-5747 UR - https://doi.org/10.2991/bep-16.2017.22 DO - 10.2991/bep-16.2017.22 ID - CAI2016/12 ER -