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Robust Processing of Visual Evoked Potentials

机译：视觉诱发电位的稳健处理

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摘要

Electrophysiology measurements attempt to get information about neural pathological conditions by measuring electrical cortical responses to appopriate stimuli. Visual Evoked Potentials (VEPs) are the responses of occipital visual cortex to time-varying images captured by human eyes. They consist of a quasi-deterministic signal embedded in a strong background noise, produced by Electro-EncephaloGraphic (EEG) activity, sensors and amplifiers.rnThe VEP analysis is useful for early diagnosis and monitoring of diabetes, neural diseases such as multiple sclerosis, vascular diseases of the brain, check of the visual acuity in non-collaborative patients (children, old-aged people with degenerative neural diseases), and as a support for medico-legal investigations.rnThe signal-to-noise ratio is usually very low and a statistical averaging on multiple stimuli is required in order to recover an acceptable VEP waveform. However the background noise is highly non stationary, clearly not Gaussian distributed and in some realization there is a strong quasi-periodic interference due to cerebral a-waves.rnMoreover, the loss of attention and blinking may cause the lack or the weakening of the VEP signal in some trials. This impacts the reliability of standard (unweighted) average commonly used to reduce noise effects. So estimators that are resistant to the presence of outliers in the data set should be used for VEP preprocessing. In this work, traditional robust estimators, such as the trimmed mean, and the median are compared with a modern neural version of the Iteratively Re weighted Least Squares (IRLS) which uses the recently developed Block Recursive Least Squares (BRLS) learning algorithm.rnThe same IRLS is used for the final deconvolution of brain spikes, that are the main target of VEP analysis.
机译：电生理学测量试图通过测量皮层对适当刺激的电响应来获取有关神经病理状况的信息。视觉诱发电位（VEP）是枕部视觉皮层对人眼捕获的时变图像的响应。它们由嵌入在强烈背景噪声中的准确定性信号组成，该信号由脑电图（EEG）活动，传感器和放大器产生。rnVEP分析可用于糖尿病的早期诊断和监测，多发性硬化症，血管等神经疾病脑部疾病，检查非协作患者（儿童，患有退行性神经疾病的老年人）的视敏度，并作为法医学研究的依据。rn信噪比通常很低，并且为了恢复可接受的VEP波形，需要对多个刺激进行统计平均。然而，背景噪声是高度不稳定的，显然不是高斯分布的，并且在某种程度上会由于脑a波而产生强烈的准周期干扰。此外，注意力不集中和眨眼可能会导致VEP缺乏或减弱。在一些试验中发出信号。这会影响通常用于减少噪声影响的标准（未加权）平均值的可靠性。因此，应使用可抵抗数据集中异常值的估计量进行VEP预处理。在这项工作中，将传统的鲁棒估计量（例如修剪后的均值和中位数）与使用重新开发的块递归最小二乘（BRLS）学习算法的迭代神经加权最小二乘（IRLS）的现代神经版本进行比较。相同的IRLS用于最终的脑波峰解卷积，这是VEP分析的主要目标。

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  • 来源
    《Neural nets Wirn Vietri-98》|1998年|310-318|共9页
  • 会议地点 Vietri sul Mare(IT)
  • 作者

    E.D. Di Claudio; G. Falvo; R. Parisi; R Perilli; G. Orlandi;

  • 作者单位

    INFOCOM Dpt. - University of Rome 'La Sapienza' Mail address: Via Eudossiana 18, I-00184 Roma - Italy;

    INFOCOM Dpt. - University of Rome 'La Sapienza' Mail address: Via Eudossiana 18, I-00184 Roma - Italy;

    INFOCOM Dpt. - University of Rome 'La Sapienza' Mail address: Via Eudossiana 18, I-00184 Roma - Italy;

    INFOCOM Dpt. - University of Rome 'La Sapienza' Mail address: Via Eudossiana 18, I-00184 Roma - Italy;

    INFOCOM Dpt. - University of Rome 'La Sapienza' Mail address: Via Eudossiana 18, I-00184 Roma - Italy;

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  • 正文语种 eng
  • 中图分类 自动化系统理论;
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