Fast and Scalable Multi-Way Analysis of Massive Neural Data
Fast and Scalable Multi-Way Analysis of Massive Neural Data
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DOI：
10.1109/tc.2013.2295806
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发表时间：
2015-03
影响因子：
3.7
通讯作者：
Dan Chen;Xiaoli Li;Lizhe Wang;S. Khan;Juan Wang;Ke Zeng;Chang Cai
中科院分区：
文献类型：
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作者：
Dan Chen;Xiaoli Li;Lizhe Wang;S. Khan;Juan Wang;Ke Zeng;Chang Cai
近年来，随着神经科学研究和实践的进步，神经数据的多模式、高密度分析已成为一个趋势。迫切需要一种方法来准确而独特地捕获特征，而不会丢失或破坏空间，时间和频率模式之间的相互作用（通常）。此外，该方法必须能够快速分析数十甚至数百个通道的指数级增长的尺度和大小的神经数据，以便及时得出结论和决策。并行因子分析（PARAFAC）是多路数据分析的一个重要方法，它在脑电图（EEG）分解中显示出其有效性。然而，传统的PARAFAC由于复杂度高，只适合离线数据分析，随着数据量的增加，计算量为O（n2）。本研究提出了一种基于图形处理器（GPGPU）的通用计算支持的大规模PARAFAC方法。与传统的基于cpu的平台上运行的PARAFAC相比，新方法在运行时性能上显著提高360倍，在所有维度上有效扩展400倍。此外，所提出的方法构成了分析癫痫患者耳蜗电图（ECoG）记录模型的基础，该模型在癫痫状态检测中被证明是有效的。该模型的时间演化与临床观察结果有很好的相关性。此外，在初始阶段，频率特征稳定且高。此外，空间特征明确地识别了神经活动在不同大脑区域之间的传播。该模型支持>1的ECoG实时分析；000个频道在廉价和可用的网络基础设施上。
Analysis of neural data with multiple modes and high density has recently become a trend with the advances in neuroscience research and practices. There exists a pressing need for an approach to accurately and uniquely capture the features without loss or destruction of the interactions amongst the modes (typically) of space, time, and frequency. Moreover, the approach must be able to quickly analyze the neural data of exponentially growing scales and sizes, in tens or even hundreds of channels, so that timely conclusions and decisions may be made. A salient approach to multi-way data analysis is the parallel factor analysis (PARAFAC) that manifests its effectiveness in the decomposition of the electroencephalography (EEG). However, the conventional PARAFAC is only suited for offline data analysis due to the high complexity, which computes to be O(n2) with the increasing data size. In this study, a large-scale PARAFAC method has been developed, which is supported by general-purpose computing on the graphics processing unit (GPGPU). Comparing to the PARAFAC running on conventional CPU-based platform, the new approach dramatically excels by >360 times in run-time performance, and effectively scales by >400 times in all dimensions. Moreover, the proposed approach forms the basis of a model for the analysis of electrocochleography (ECoG) recordings obtained from epilepsy patients, which proves to be effective in the epilepsy state detection. The time evolutions of the proposed model are well correlated with the clinical observations. Moreover, the frequency signature is stable and high in the ictal phase. Furthermore, the spatial signature explicitly identifies the propagation of neural activities among various brain regions. The model supports real-time analysis of ECoG in > 1;000 channels on an inexpensive and available cyber-infrastructure.