一种基于局部密度的自适应眼电伪迹去除方法

李沛洋; 高晓辉; 朱鹏程; 黄伟杰; 李存波; 司亚静; 徐鹏; 田银

doi:10.11999/JEIT210845

一种基于局部密度的自适应眼电伪迹去除方法

doi: 10.11999/JEIT210845

1.
重庆邮电大学生物信息学院重庆 400065
2.
电子科技大学生命科学与技术学院成都 610054
3.
新乡医学院心理学院新乡 453003

基金项目: 国家自然科学基金青年基金(61901077)

详细信息

作者简介:
李沛洋：男，1989年生，博士，研究方向为鲁棒脑网络估计算法、电特征提取和模式识别

高晓辉：男，1998年生，硕士生，研究方向为贝叶斯参数估计

黄伟杰：男，1999年生，硕士生，研究方向为神经网络、深度学习

李存波：男，1999年生，硕士生，研究方向为神经网络、深度学习

司亚静：女，1990年生，博士，研究方向为决策脑电认知机制和精神分裂症患者冷执行功能

徐鹏：男，1977年生，博士，研究方向为脑机交互技术、生理信号处理

田银：女，1972年生，博士，研究方向为脑电信号处理、认知神经科学

通讯作者:
田银　tianyin@cqupt.edu.cn

中图分类号: TN911.72
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- 被引次数: 0
出版历程
- 收稿日期: 2021-08-18
- 修回日期: 2022-01-15
- 录用日期: 2022-01-17
- 网络出版日期: 2022-01-22
- 刊出日期: 2022-02-25

An Adaptive EOG Removal Method Based on Local Density

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)

摘要

摘要: 脑电信号幅值微弱且信噪比低易受到多种伪迹影响。其中，眼电伪迹幅值高、随机性强，常使脑电信号产生明显畸变，对信号的后续分析将产生极大的影响。传统伪迹去除方法难以精确定位伪迹成分，导致过多有效信息丢失。针对上述问题，该文提出一种基于数据驱动的自适应伪迹定位和去除方法。该方法将局部密度引入独立成分分析（ICA）并通过聚类分析自适应估计辨识脑电和噪声成分的阈值，最终实现了眼电伪迹的精准定位和去除。通过仿真和真实实验，该文对比了所提方法与传统伪迹去除方法在峰值信噪比、均方误差、互信息等量化指标下的性能差异，并通过统计检验揭示了所提方法相比于其他方法在信号恢复方面的显著性优势。
- 自适应阈值 /
- 局部密度 /
- 伪迹定位 /
- 脑电 /
- 独立成分分析
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)

HTML全文

图 1 脑电电极位置图

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图 2 静息态脑电采集流程

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图 3 仿真数据生成图

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图 4 仿真实验流程图

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图 5 功率谱密度对比图

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图 6 ATICA 伪迹去除过程

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图 7 真实实验流程图

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图 8 EEG 甄别伪迹成分效果对比图

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表 1 仿真实验均值指标对比

	Zeroing ICA	wICA	本文ATICA
SNR	3.96±0.41$ \boldsymbol{\psi } $	4.70±0.53$ \boldsymbol{\psi } $	5.73±0.40
MSE	5.46±0.25$ \boldsymbol{\psi } $	3.56±0.95$ \boldsymbol{\psi } $	2.69±0.25
PSNR	45.15±0.41$ \boldsymbol{\psi } $	45.89±0.53$ \boldsymbol{\psi } $	46.93±0.40
RMSE	1.80±0.06$ \boldsymbol{\psi } $	1.56±0.13$ \boldsymbol{\psi } $	1.37±0.06
RCE	0.80±0.02$ \boldsymbol{\psi } $	0.52±0.03$ \boldsymbol{\psi } $	0.87±0.03

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表 2 真实实验PCC/MI均值对比

电极区域	指标	Zeroing ICA	wICA	ATICA
额顶叶(FP)	PCC MI	0.63±0.09$ \boldsymbol{\psi } $ 0.82±0.13$ \boldsymbol{\psi } $	0.63±0.11$ \boldsymbol{\psi } $ 0.86±0.16$ \boldsymbol{\psi } $	0.68±0.09 1.05±0.17
前额叶(AF)	PCC MI	0.69±0.09$ \boldsymbol{\psi } $ 0.88±0.14$ \boldsymbol{\psi } $	0.71±0.11$ \boldsymbol{\psi } $ 0.98±0.18$ \boldsymbol{\psi } $	0.74±0.09 1.15±0.17
额叶(F)	PCC MI	0.81±0.05$ \boldsymbol{\psi } $ 0.97"±0.16$ \boldsymbol{\psi } $	0.85±0.06$ \boldsymbol{\psi } $ 1.17±0.24$ \boldsymbol{\psi } $	0.88±0.04 1.35±0.23
额颞叶(FT)	PCC MI	0.85±0.04$ \boldsymbol{\psi } $ 1.01±0.16$ \boldsymbol{\psi } $	0.85±0.05$ \boldsymbol{\psi } $ 1.24±0.31$ \boldsymbol{\psi } $	0.88±0.04 1.35±0.28
额中央(FC)	PCC MI	0.84±0.05$ \boldsymbol{\psi } $ 1.02±0.16$ \boldsymbol{\psi } $	0.88±0.05$ \boldsymbol{\psi } $ 1.26±0.26$ \boldsymbol{\psi } $	0.91±0.04 1.45±0.23
颞叶(T)	PCC MI	0.87±0.04$ \boldsymbol{\psi } $ 1.09±0.17$ \boldsymbol{\psi } $	0.91±0.04$ \boldsymbol{\psi } $ 1.31±0.32$ \boldsymbol{\psi } $	0.94±0.03 1.55±0.32
额叶中央沟(C)	PCC MI	0.85±0.04$ \boldsymbol{\psi } $ 1.04±0.15$ \boldsymbol{\psi } $	0.90±0.05$ \boldsymbol{\psi } $ 1.28±0.32$ \boldsymbol{\psi } $	0.93±0.04 1.50±0.33
颞顶叶(TP)	PCC MI	0.88±0.04$ \boldsymbol{\psi } $ 1.07±0.15$ \boldsymbol{\psi } $	0.91±0.03$ \boldsymbol{\psi } $ 1.28±0.27$ \boldsymbol{\psi } $	0.94±0.02 1.53±0.27
中央顶叶(CP)	PCC MI	0.86±0.04$ \boldsymbol{\psi } $ 1.07±0.17$ \boldsymbol{\psi } $	0.91±0.03$ \boldsymbol{\psi } $ 1.28±0.24$ \boldsymbol{\psi } $	0.94±0.03 1.53±0.24
顶叶(P)	PCC MI	0.87±0.04$ \boldsymbol{\psi } $ 1.06±0.15$ \boldsymbol{\psi } $	0.91±0.03$ \boldsymbol{\psi } $ 1.27±0.26$ \boldsymbol{\psi } $	0.94±0.02 1.50±0.24
枕叶(O)	PCC MI	0.85±0.04$ \boldsymbol{\psi } $ 1.02±0.15$ \boldsymbol{\psi } $	0.89±0.04$ \boldsymbol{\psi } $ 1.21±0.30$ \boldsymbol{\psi } $	0.92±0.03 1.43±0.31
通道指标均值	PCC MI	0.82±0.05$ \boldsymbol{\psi } $ 1.00±0.16$ \boldsymbol{\psi } $	0.86±0.05$ \boldsymbol{\psi } $ 1.20±0.26$ \boldsymbol{\psi } $	0.89±0.04 1.41±0.26

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