基于频谱的静息状态脑电分析过程的改进 基于LES的权重投票

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摘要翻译：
脑电是一种记录大脑生物电活动的非侵入性技术，在人机交互、神经科学等各个领域都有潜在的应用前景。然而，脑电信号成分复杂，振幅低，信噪比低，是分析脑电信号的难点。现有的一些分析方法是基于特征提取和机器学习来区分样本所属的精神分裂症阶段。然而，医学研究要求使用机器学习不仅要给出更准确的分类结果，还要给出可应用于病理研究的结果。本研究的主要目的是在基于LES特征提取的更有效的分类方法的基础上，获得各频段对精神分裂症时相分类影响的权值，并对权值进行处理和应用，以提高机器学习分类的准确率。我们提出了一种权重投票的方法，利用分类结果获得子带特征的权重，以拟合脑电数据的实际类别，并利用权重进行重新分类。通过这种方法，我们首先可以获得每个波段对区分精神分裂症三个阶段的影响，并分析波段特征对精神分裂症风险的影响，有助于精神病理学的研究。我们的结果表明，低伽马能带重量的变化与HC、CHR和FES之间的差值有很高的相关性。如果将根据权重修正后的特征用于重新分类，结果的准确性将比原分类器有所提高，从而证实了权重分布的作用。
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英文标题：
《Improvement of Resting-state EEG Analysis Process with Spectrum
  Weight-Voting based on LES》
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作者：
Yumeng Ye, Haichun Liu, TianHong Zhang, Changchun Pan, Genke Yang,
  JiJun Wang, Robert C. Qiu
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最新提交年份：
2018
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分类信息：

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：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
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英文摘要：
  EEG is a non-invasive technique for recording brain bioelectric activity, which has potential applications in various fields such as human-computer interaction and neuroscience. However, there are many difficulties in analyzing EEG data, including its complex composition, low amplitude as well as low signal-to-noise ratio. Some of the existing methods of analysis are based on feature extraction and machine learning to differentiate the phase of schizophrenia that samples belong to. However, medical research requires the use of machine learning not only to give more accurate classification results, but also to give the results that can be applied to pathological studies. The main purpose of this study is to obtain the weight values as the representation of influence of each frequency band on the classification of schizophrenia phases on the basis of a more effective classification method using the LES feature extraction, and then the weight values are processed and applied to improve the accuracy of machine learning classification. We propose a method called weight-voting to obtain the weights of sub-bands features by using results of classification for voting to fit the actual categories of EEG data, and using weights for reclassification. Through this method, we can first obtain the influence of each band in distinguishing three schizophrenia phases, and analyze the effect of band features on the risk of schizophrenia contributing to the study of psychopathology. Our results show that there is a high correlation between the change of weight of low gamma band and the difference between HC, CHR and FES. If the features revised according to weights are used for reclassification, the accuracy of result will be improved compared with the original classifier, which confirms the role of the band weight distribution.
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PDF链接：
https://arxiv.org/pdf/1712.07369

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关键词：les Applications Optimization Quantitative Neuroscience 特征 low 学习 weight 精神分裂症