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首页> 外文期刊>Inverse Problems: An International Journal of Inverse Problems, Inverse Methods and Computerised Inversion of Data >Multichannel blind deconvolution via maximum likelihood estimator: application in neural recordings
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Multichannel blind deconvolution via maximum likelihood estimator: application in neural recordings

机译：多通道盲卷积通过最大似然估计：在神经录音中的应用

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摘要

In many multidimensional data such as radar recordings and astrophotography images, the receiver sensors record a linear mixture of signals propagated by a few activities, and the signal of each activity is repetition of a specific waveform at different times and with different amplitudes. The goal of multichannel blind deconvolution problem is retrieving the characteristics of the mentioned activities from the recorded signals. This problem is ill-posed without additional constraints, hence, different constraints are considered for this problem depending on the considered application. In this study, we propose a maximum likelihood framework for solving multichannel blind deconvolution problem when (1) the waveforms of the activities are time-limited signals, (2) the waveforms occur only a few times in the signal of each activity, or in other words, the occurrence times of the activities are sparse signals, and (3) there is no overlap between two consecutive occurrences of the waveform in the signal of each activity. The considered scenario can be adapted to several applications especially to neural recordings. We verify the efficiency of the proposed framework using simulations. Moreover, we apply the proposed framework on a real neural data set, and show how the obtained results can be employed to analyze the data from signal processing point of view.
机译：在诸如雷达记录和天空摄影图像的许多多维数据中，接收器传感器记录由少数活动传播的信号的线性混合，并且每次活动的信号在不同时间和不同幅度下重复特定波形。多通道盲卷积问题的目标是从记录的信号检索提到的活动的特征。此问题在没有额外的约束的情况下没有释放，因此，根据所考虑的应用程序考虑此问题的不同约束。在本研究中，当（1）活动的波形是时间有限的信号时，我们提出了最大的似然框架，用于解决多通道盲卷积问题，（1）波形是时间有限的信号，（2）波形仅发生在每个活动的信号中的几次，或者其他单词，活动的发生时间是稀疏信号，并且（3）在每个活动的信号中的两个连续出现波形中没有重叠。所考虑的场景可以适应若干应用，尤其是神经记录。我们使用模拟验证所提出的框架的效率。此外，我们在真正的神经数据集上应用所提出的框架，并展示如何采用所获得的结果来分析来自信号处理的观点的数据。

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