ECoG数据的可解释可视化与高阶降维
Interpretable Visualization and Higher-Order Dimension Reduction for ECoG Data
摘要：
皮层电图（ECoG）技术通过在神经外科手术期间直接放置在皮层表面上的电极来测量人脑中的电活动。通过其以快速时间分辨率记录活动的能力，ECoG实验使科学家能够更好地了解人类大脑如何处理语音。就其性质而言，ECoG数据很难让神经科学家直接解释，主要有两个原因。首先，ECoG数据往往很大，因为每个单独的实验都会产生高达几千兆字节的数据。其次，ECoG数据具有复杂的高阶性质。在信号处理之后，这种类型的数据可以被组织为具有表示试验、电极、频率和时间的维度的4向张量。在本文中，我们开发了一种可解释的降维方法，称为正则化高阶主成分分析，以及正则化高阶偏最小二乘的扩展，允许神经科学家探索和可视化ECoG数据。我们的方法采用了稀疏和功能性的Candecomp-Parafac（CP）分解，该分解结合了稀疏性来选择相关的电极和频带，以及随时间和频率的平滑度，从而产生直接可解释的因素。我们证明了我们的方法的性能和可解释性与ECoG的情况下研究人类语音的音频和视觉处理。
英文摘要：
ElectroCOrticoGraphy (ECoG) technology measures electrical activity in the human brain via electrodes placed directly on the cortical surface during neurosurgery. Through its capability to record activity at a fast temporal resolution, ECoG experiments have allowed scientists to better understand how the human brain processes speech. By its nature, ECoG data is difficult for neuroscientists to directly interpret for two major reasons. Firstly, ECoG data tends to be large in size, as each individual experiment yields data up to several gigabytes. Secondly, ECoG data has a complex, higher-order nature. After signal processing, this type of data may be organized as a 4-way tensor with dimensions representing trials, electrodes, frequency, and time. In this paper, we develop an interpretable dimension reduction approach called Regularized Higher Order Principal Components Analysis, as well as an extension to Regularized Higher Order Partial Least Squares, that allows neuroscientists to explore and visualize ECoG data. Our approach employs a sparse and functional Candecomp-Parafac (CP) decomposition that incorporates sparsity to select relevant electrodes and frequency bands, as well as smoothness over time and frequency, yielding directly interpretable factors. We demonstrate the performance and interpretability of our method with an ECoG case study on audio and visual processing of human speech.
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