An Adaptive EOG Removal Method Based on Local Density

LI Peiyang; GAO Xiaohui; ZHU Pengcheng; HUANG Weijie; LI Cunbo; SI Yajing; XU Peng; TIAN Yin

doi:10.11999/JEIT210845

Volume 44 Issue 2

Feb. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 464-476

LI Peiyang, GAO Xiaohui, ZHU Pengcheng, HUANG Weijie, LI Cunbo, SI Yajing, XU Peng, TIAN Yin. An Adaptive EOG Removal Method Based on Local Density[J]. Journal of Electronics & Information Technology, 2022, 44(2): 464-476. doi: 10.11999/JEIT210845

Citation:

LI Peiyang, GAO Xiaohui, ZHU Pengcheng, HUANG Weijie, LI Cunbo, SI Yajing, XU Peng, TIAN Yin. An Adaptive EOG Removal Method Based on Local Density[J]. Journal of Electronics & Information Technology, 2022, 44(2): 464-476. doi: 10.11999/JEIT210845

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An Adaptive EOG Removal Method Based on Local Density

doi: 10.11999/JEIT210845

1.
School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
3.
School of Psychology, Xinxiang Medical University, Xinxiang 453003, China

Funds: The National Natural Science Foundation of China Youth Fund (61901077)

Received Date: 2021-08-18
Accepted Date: 2022-01-17
Rev Recd Date: 2022-01-15

Available Online: 2022-01-22

Publish Date: 2022-02-25

Abstract

Abstract

EEG (ElectroEncephaloGram) signal is susceptible to various of artifacts due to its low amplitude and poor SNR (Signal-Noise Ratio). Among this noise, the ocular artifacts usually hold higher amplitude and strong randomness which would cause serious distortion on EEG signal, and result in great influence on the subsequent analysis. However, traditional methods fail to locate the artifacts components accurately, leading to the loss of the efficient signal components. In order to solve the above problem, this paper proposes a data-driven based automatically artifact-localization-and-removement method. In this paper, the local density is firstly introduced into ICA (Independent Component Analysis) so as to estimate the adaptive threshold with clustering strategy. This adaptive threshold would be further used to noise localization and removal. Subsequently, this paper compared the performance differences between the proposed method and the traditional methods through simulation and the real resting-state EEG experiments. The results with indexes such as PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Square Error), and MI (Mutual Information) quantitatively verify the significant superiority of the proposed method to other ICA-based ocular artifacts removal strategies through statistical analysis.
- Adaptive threshold,
- Local density,
- Artifact localization,
- ElectroEncephaloGram (EEG),
- Independent Component Analysis (ICA)

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References(52)

References

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