Enhancing source separation quality via optimal sensor placement in noisy environments

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Mohammad Sadeghi , Bertrand Rivet , Massoud Babaie-Zadeh
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引用次数: 0

Abstract

The paper aims to bridge a part of the gap between source separation and sensor placement studies by addressing a novel problem: “Predicting optimal sensor placement in noisy environments to improve source separation quality”. The structural information required for optimal sensor placement is modeled as the spatial distribution of source signal gains and the spatial correlation of noise. The sensor positions are predicted by optimizing two criteria as measures of separation quality, and a gradient-based global optimization method is developed to efficiently address this optimization problem. Numerical results exhibit superior performance when compared with classical sensor placement methodologies based on mutual information, underscoring the critical role of sensor placement in source separation with noisy sensor measurements. The proposed method is applied to actual electroencephalography (EEG) data to separate the P300 source components in a brain-computer interface (BCI) application. The results show that when the sensor positions are chosen using the proposed method, to reach a certain level of spelling accuracy, fewer sensors are required compared with standard sensor locations.

在嘈杂环境中通过优化传感器位置提高信号源分离质量
本文旨在通过解决一个新问题:"预测噪声环境中的最佳传感器位置以提高声源分离质量",弥补声源分离和传感器位置研究之间的部分差距。优化传感器位置所需的结构信息被建模为声源信号增益的空间分布和噪声的空间相关性。通过优化作为分离质量衡量标准的两个标准来预测传感器位置,并开发了一种基于梯度的全局优化方法来有效解决这一优化问题。与基于互信息的传统传感器位置放置方法相比,数值结果显示出更优越的性能,突出了传感器位置放置在噪声传感器测量的声源分离中的关键作用。我们将所提出的方法应用于实际脑电图(EEG)数据,以分离脑机接口(BCI)应用中的 P300 源成分。结果表明,当使用所提出的方法选择传感器位置时,要达到一定的拼写精度,与标准传感器位置相比,所需的传感器数量更少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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