Online artifact removal for brain-computer interfaces using support vector machines and blind source separation.
Online artifact removal for brain-computer interfaces using support vector machines and blind source separation.
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DOI：
10.1155/2007/82069
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发表时间：
2007
影响因子：
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通讯作者：
Rosenstiel, Wolfgang
中科院分区：
文献类型：
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作者：
Halder, Sebastian;Bensch, Michael;Mellinger, Jurgen;Bogdan, Martin;Kubler, Andrea;Birbaumer, Niels;Rosenstiel, Wolfgang
我们提出了盲源分离（BSS）和独立分量分析（伊卡）（信号分解为文物和nonartifacts）与支持向量机（SVM）（自动分类），设计用于在线使用的组合。为了选择一个合适的BSS/伊卡方法，三个伊卡算法（JADE，Infomax和FastICA）和一个BSS算法（AMUSE）进行了评估，以确定他们的能力，隔离肌电（EMG）和眼电（EOG）的工件到各个组件。所选择的BSS/伊卡方法与SVM训练分类EMG和EOG伪影，这使得使用的方法作为一个过滤器的测量与在线反馈的实施，进行说明。在三个BCI数据集上评估该滤波器，作为该方法的概念验证。
We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.