摘要翻译：
目的。儿童大脑发育过程中不断变化的生理特性对新的数据密集型技术提出了挑战，如脑机接口(BCI)。改进这些技术中的信号处理方法，使其对发育变化更加敏感，有助于改善它们在儿科人群中的功能和可用性。通过张量分析，利用脑电数据的多维结构，提出了一种提取儿童静息状态脑电数据中相关发育特征的框架。方法。采用发展的两步约束平行因子张量分解(PARAFAC)对来自不同发育状态和人群的三个儿科数据集进行分析。数据集包括缪尔·麦克斯韦癫痫中心、波士顿麻省理工学院儿童医院和儿童心智研究所，分别概述了两个受损人群和一个健康人群。在数据集中，交叉验证使用支持向量机(SVM)对非折叠数据进行分类，预测被试的年龄作为发展的代理度量。T-分布随机邻域嵌入（t-SNE）地图通过高维特征结构的可视化补充了分类分析。主要成果。成功地为每个数据集的开发条件识别了开发敏感特征。支持向量机的分类精度和错误分类成本在健康和受损儿童人群中都有显著提高。t-SNE图谱显示，适当的张量因子分解是提取发育特征的关键。意义。所描述的方法是一个有希望的工具，将儿童脑电图的独特发展特征纳入新技术，如脑机接口及其应用。
---
英文标题：
《Tensor-driven extraction of developmental features from varying
  paediatric EEG datasets》
---
作者：
Eli Kinney-Lang, Loukianos Spyrou, Ahmed Ebied, Richard FM Chin,
  Javier Escudero
---
最新提交年份：
2017
---
分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Neurons and Cognition        神经元与认知
分类描述：Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触，皮层，神经元动力学，神经网络，感觉运动控制，行为，注意
--
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。
--

---
英文摘要：
  Objective. Consistently changing physiological properties in developing children's brains challenges new data heavy technologies, like brain-computer interfaces (BCI). Advancing signal processing methods in such technologies to be more sensitive to developmental changes could help improve their function and usability in paediatric populations. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis offers a framework to extract relevant developmental features present in paediatric resting-state EEG datasets. Methods. Three paediatric datasets from varying developmental states and populations were analyzed using a developed two-step constrained Parallel Factor (PARAFAC) tensor decomposition. The datasets included the Muir Maxwell Epilepsy Centre, Children's Hospital Boston-MIT and the Child Mind Institute, outlining two impaired and one healthy population, respectively. Within dataset cross-validation used support vector machines (SVM) for classification of out-of-fold data predicting subject age as a proxy measure of development. t-distributed Stochastic Neighbour Embedding (t-SNE) maps complemented classification analysis through visualization of the high-dimensional feature structures. Main Results. Development-sensitive features were successfully identified for the developmental conditions of each dataset. SVM classification accuracy and misclassification costs were improved significantly for both healthy and impaired paediatric populations. t-SNE maps revealed suitable tensor factorization was key in extracting developmental features. Significance. The described methods are a promising tool for incorporating the unique developmental features present throughout childhood EEG into new technologies like BCI and its applications.
---
PDF链接：
https://arxiv.org/pdf/1712.07443