改善脑电信号质量:减少伪差的盲源分离和变异模式破坏混合方法

H. Massar, T. B. Drissi, B. Nsiri, Mounia Miyara
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引用次数: 0

摘要

脑电图(EEG)是研究大脑活动的重要工具,但它经常会遇到扭曲有意义神经信号的伪影。本文通过一种独特的混合方法,将变异模式分解(VMD)技术与盲源分离(BSS)算法相结合,解决了去除伪影的难题。VMD 因其对非线性和非稳态脑电图数据的适应性,以及其减轻模式混合和 "端点效应 "的能力而得到认可,是一种有效的预处理步骤。论文评估了 AMICA 和 AMUSE 这两种集成 BSS 算法在不同标准下的性能。通过比较欧氏距离、斯皮尔曼相关系数和均方根误差等指标,发现 AMICA 和 AMUSE 的性能相似。但是,在信号与伪差比 (SAR) 方面却存在明显的差异。当与 VMD 结合使用时,AMICA 在有效辨别和分离大脑信号与伪像方面表现出了优势,其平均值为 1.0924。本研究介绍了一种有效的混合 VMDBSS 方法,用于提高脑电信号质量。研究结果强调了 AMICA 的显著影响,特别是在去除伪像方面取得了最佳效果,其在 SAR 方面的卓越表现也说明了这一点。摘要最后强调了这些结果的重要性,强调了 AMICA 在实现最高可测量评估值方面的关键作用,使其成为脑电信号处理研究人员和从业人员的不二之选。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improvements of EEG Signal Quality: A Hybrid Method of Blind Source Separation and Variational Mode Destruction to Reduce Artifacts
The electroencephalogram (EEG) is a crucial tool for studying brain activity; yet it frequently encounters artifacts that distort meaningful neural signals. This paper addresses the challenge of artifact removal through a unique hybrid method, combining Variational Mode Decomposition (VMD) techniques with Blind Source Separation (BSS) algorithms. VMD, recognized for its adaptability to non-linear and non-stationary EEG data, as well as its ability to alleviate mode mixing and the “endpoint effect,” which serves as an effective preprocessing step. The paper evaluates the performance of two integrated BSS algorithms, AMICA and AMUSE, across various criteria. Comparisons across metrics such as Euclidean distance, Spearman correlation coefficient, and Root Mean Square Error reveal similar performance between AMICA and AMUSE. However, a distinct divergence is evident in the Signal to Artifact Ratio (SAR). When employed with VMD, AMICA demonstrates superiority in effectively discerning and segregating brain signals from artifacts, which gives a mean value of 1.0924. This study introduces a potent hybrid VMDBSS approach for enhancing EEG signal quality. The findings emphasize the notable impact of AMICA, particularly in achieving optimal results in artifact removal, as indicated by its superior performance in SAR. The abstract concludes by underlining the significance of these results, emphasizing AMICA’s pivotal role in achieving the highest measurable evaluation value, making it a compelling choice for researchers and practitioners in EEG signal processing.
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