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Multiresolution analysis of multichannel neural recordings in the context of signal detection, estimation, classification and noise suppression.

机译:在信号检测,估计,分类和噪声抑制的背景下对多通道神经记录进行多分辨率分析。

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The development of multichannel microprobe fabrication technology for recording neural activity in the brain has recently achieved a significant milestone towards integrating microimplanted device technology with research and clinical applications in neurophysiology. Recent probe designs have been able to integrate large number of sites on a single probe to provide neuroscientists with tools to record from large populations of cells. Advances in probe design are always governed by the feasibility of the associated communication and signal processing technology. Surprisingly, existing signal processing techniques are considerably behind the overwhelming advances in probe fabrication technology.; We envision the problem of optimizing the information transfer from the microdevice as three fold: Noise suppression, Signal detection, and Blind Source Separation. We demonstrate that all three goals can be achieved by merging multiresolution analysis theory with array processing theory into a novel unified framework. In the noise suppression context, we show that we can near-optimally suppress the additive correlated noise by introducing a spatio-temporal decorrelation mechanism using eigendecomposition of a discrete wavelet transform representation of the array data followed by universal thresholding, a unique property of the multiresolution analysis. In the detection context, when no apriori knowledge is given about the signal and/or the noise processes, we formulate a transform domain Generalized Likelihood Ratio Test in the array case that overcomes the problem of estimating unknown noise parameters. The Blind Source identification problem is approached within the same context using an inherent invariance property of the signal subspace across multiresolution levels that enables characterization of each neural source.; Results demonstrate that this framework provides the basis for simple and practical implementation in the structure of today's biosensor array technology without compromising issues of bandwidth, detection and classification. We show that the framework is capable of achieving substantial improvement in detection performance in severe noise conditions, and robustness to source nonstationarities and nonisotropic properties of the unknown medium under no constraints on the array design or prior knowledge of the signal parameters.
机译:记录大脑中神经活动的多通道微探针制造技术的发展,最近取得了重要的里程碑,将微植入装置技术与神经生理学的研究和临床应用相结合。最近的探针设计已经能够在单个探针上整合大量位点,从而为神经科学家提供从大量细胞中进行记录的工具。探头设计的进步始终受相关通信和信号处理技术的可行性支配。令人惊讶的是,现有的信号处理技术大大落后于探针制造技术的飞速发展。我们预想了将来自微设备的信息传输优化为三个方面的问题:噪声抑制,信号检测和盲源分离。我们证明通过将多分辨率分析理论与数组处理理论合并到一个新颖的统一框架中可以实现所有这三个目标。在噪声抑制的背景下,我们表明可以通过引入时空去相关机制,利用阵列数据的离散小波变换表示的特征分解,然后进行通用阈值处理(多分辨率的独特属性),来近乎最佳地抑制加性相关噪声。分析。在检测上下文中,当没有先验知识提供有关信号和/或噪声过程的信息时,我们在阵列情况下制定了一个变换域广义似然比测试,从而克服了估计未知噪声参数的问题。盲源识别问题是在同一上下文中使用跨多个分辨率级别的信号子空间的固有不变性来实现的,该特性能够表征每个神经源。结果表明,该框架为当今生物传感器阵列技术的结构提供了简单实用的基础,而不会影响带宽,检测和分类问题。我们表明,该框架能够在严重的噪声条件下实现检测性能的显着提高,并且能够在不受阵列设计或信号参数先验知识约束的情况下,稳健地获得未知介质的非平稳性和非各向同性特性。

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