摘要翻译：
准确、快速、可靠地对脑电图(EEG)信号进行多类分类是运动图像脑-机接口（MI-BCI）系统发展的一项具有挑战性的任务。我们提出了对不同特征提取器的改进，以及支持向量机(SVM)分类器，在训练和测试过程中同时提高分类精度和执行时间。重点研究了常用的空间模式(CSP)和黎曼协方差方法，并将这两种特征提取方法推广到多尺度的时间和光谱情况。在4类BCI竞争IV-2a数据集上，多尺度CSP特征的分类准确率达到73.70$\pm$15.90%（9个被试的平均$\pm$标准差），超过了现有方法[1]的70.6$\pm$14.70%。黎曼协方差特性优于CSP,实现了74.27$\pm$15.5%的精确度，在训练中执行速度快9倍，在测试中执行速度快4倍。对黎曼特征使用更多的时间窗口可获得75.47$\pm$12.8%的准确率，测试速度比CSP快1.6倍。
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英文标题：
《Fast and Accurate Multiclass Inference for MI-BCIs Using Large
  Multiscale Temporal and Spectral Features》
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作者：
Michael Hersche, Tino Rellstab, Pasquale Davide Schiavone, Lukas
  Cavigelli, Luca Benini, Abbas Rahimi
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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：
  Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and spectral cases. The multiscale CSP features achieve 73.70$\pm$15.90% (mean$\pm$ standard deviation across 9 subjects) classification accuracy that surpasses the state-of-the-art method [1], 70.6$\pm$14.70%, on the 4-class BCI competition IV-2a dataset. The Riemannian covariance features outperform the CSP by achieving 74.27$\pm$15.5% accuracy and executing 9x faster in training and 4x faster in testing. Using more temporal windows for Riemannian features results in 75.47$\pm$12.8% accuracy with 1.6x faster testing than CSP.
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PDF链接：
https://arxiv.org/pdf/1806.06823