摘要翻译：
如何处理多维数据集中的噪声脑电测量是当前脑机接口研究的一个热点问题。另一方面，近年来，利用张量分解模型来捕捉多维信号中条目之间的复杂关系的多维信号补全算法取得了重大进展。本文提出在运动图像脑机接口系统中，将张量补全应用于脑电数据，以提高测量值受损时的分类性能。噪声测量被认为是从张量分解模型中推断出来的未知数。我们评估了最近提出的四种张量补全算法和一种简单的插值策略的性能，首先使用随机缺失项，然后将缺失样本约束为具有特定结构（随机缺失通道），这在BCI应用中是一种更现实的假设。我们测量了这些算法从观测数据重建张量的能力。然后，我们在一个缺失样本的BCI实验中测试了想象运动的分类精度。我们证明了对于随机缺失项，所有的张量补全算法都能恢复缺失样本，与简单的插值方法相比，提高了分类性能。对于随机缺失通道的情况，我们证明了张量补全算法有助于重建缺失通道，显著提高了运动图像分类的准确性，但与干净数据不在同一水平。张量完备算法在实际的BCI应用中是有用的。所提出的策略可以允许使用运动图像BCI系统，即使当EEG数据受到缺失通道和/或样本的高度影响时，避免在校准阶段需要新的采集。
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英文标题：
《Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion
  Approach》
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作者：
Jordi Sole-Casals, Cesar F. Caiafa, Qibin Zhao and Adrzej Cichocki
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最新提交年份：
2018
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  One of the current issues in Brain-Computer Interface is how to deal with noisy Electroencephalography measurements organized as multidimensional datasets. On the other hand, recently, significant advances have been made in multidimensional signal completion algorithms that exploit tensor decomposition models to capture the intricate relationship among entries in a multidimensional signal. We propose to use tensor completion applied to EEG data for improving the classification performance in a motor imagery BCI system with corrupted measurements. Noisy measurements are considered as unknowns that are inferred from a tensor decomposition model. We evaluate the performance of four recently proposed tensor completion algorithms plus a simple interpolation strategy, first with random missing entries and then with missing samples constrained to have a specific structure (random missing channels), which is a more realistic assumption in BCI Applications. We measured the ability of these algorithms to reconstruct the tensor from observed data. Then, we tested the classification accuracy of imagined movement in a BCI experiment with missing samples. We show that for random missing entries, all tensor completion algorithms can recover missing samples increasing the classification performance compared to a simple interpolation approach. For the random missing channels case, we show that tensor completion algorithms help to reconstruct missing channels, significantly improving the accuracy in the classification of motor imagery, however, not at the same level as clean data. Tensor completion algorithms are useful in real BCI applications. The proposed strategy could allow using motor imagery BCI systems even when EEG data is highly affected by missing channels and/or samples, avoiding the need of new acquisitions in the calibration stage.
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PDF链接：
https://arxiv.org/pdf/1806.05017