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首页> 外文会议>Proceedings of the 17th IASTED international conference on robotics applications ; Proceedings of the 11th IASTED international conference on biomedical engineering >KMEANS-ICA BASED AUTOMATIC METHOD FOR EOG DENOISING IN MULTI-CHANNEL EEG RECORDINGS
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KMEANS-ICA BASED AUTOMATIC METHOD FOR EOG DENOISING IN MULTI-CHANNEL EEG RECORDINGS

机译：基于KMEANS-ICA的多通道脑电图记录自动除噪方法

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摘要

Electroencephalogram (EEG) recordings are contaminated by different internal and external noises and interferences. Therefore, they should be manipulated in order to restore them from these artifacts that could be eye blinks, electrocardiogram (ECG) and many others. Recent research is mainly oriented toward implementing methods in order to remove ocular artifacts whose frequency band overlap with the EEG frequency of interest. Independent Component Analysis (ICA) has already shown to be an effective way for removing the activity of these artifacts. However, when implementing an ICA-based method, the key relies on how to identify the ocular artifact components. Based on the components characteristics, different features such as correlation coefficients, distribution ratio, and maximum value have been identified in order to recognize in an automatic way the artifactual components and their subtraction from the original space to get ocular artifacts free EEG signals. Artifactual components were identified using an adaptive thresholding by means of K-means clustering. Qualitative and quantitative techniques of evaluation are presented and give promising results. The classification accuracy based on the correlation feature reached 99.54%.
机译：脑电图（EEG）记录受到不同的内部和外部噪声和干扰的污染。因此，应该对它们进行操作，以使其从可能是眨眼，心电图（ECG）等许多伪影中恢复出来。最近的研究主要集中在实现方法上，以去除频带与所关注的EEG频率重叠的眼伪影。独立成分分析（ICA）已被证明是消除这些工件活动的有效方法。但是，在实施基于ICA的方法时，关键取决于如何识别眼部伪影成分。基于组件的特征，已识别了不同的特征，例如相关系数，分布比和最大值，以便以自动方式识别人为因素及其从原始空间中减去后的结果，从而获得无眼神器的EEG信号。通过K均值聚类，使用自适应阈值识别伪影成分。提出了定性和定量的评估技术，并给出了可喜的结果。基于相关特征的分类精度达到99.54％。

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