论文标题
卷积神经网络，用于自动检测来自脑电图信号头皮地形中的独立组件的伪影
Convolutional Neural Networks for Automatic Detection of Artifacts from Independent Components Represented in Scalp Topographies of EEG Signals
论文作者
论文摘要
脑电图（EEG）通过使用放置在头皮上的传感器来实时测量电脑活动。由于眼动和眨眼，肌肉/心脏活动和通用电动干扰，伪影必须被识别并消除，以正确解释EEG的有用脑信号（UBS）。独立的组件分析（ICA）有效地将信号分为独立的组件（IC）（ICS），其对2D头皮形状（图像）（也称为topoplots）进行重新投影，允许识别/单独的人工制品和UBS。到目前为止，人类专家以视觉上进行了脑电图标准的IC Tupoplot分析，因此在自动，快速响应的脑电图中不可用。我们为基于2D卷积神经网络（CNN）的IC topoplots识别的脑电图识别的脑电图识别的全自动和有效框架，能够在4类中划分topoplots：3种类型的工件和ubs。描述了框架设置，并与其他竞争策略获得的结果进行了，讨论和比较。在公共脑电图数据集上进行的实验显示，总体准确性高于98％，在标准PC上采用1.4秒的时间来对32个topoplots进行分类，即驱动32个传感器的脑电图系统。尽管不是实时的，但提出的框架足够有效，可以在基于快速响应的脑电图界面（BCI）中使用，并且比基于IC的其他自动方法更快。
Electroencephalography (EEG) measures the electrical brain activity in real-time by using sensors placed on the scalp. Artifacts, due to eye movements and blink, muscular/cardiac activity and generic electrical disturbances, have to be recognized and eliminated to allow a correct interpretation of the useful brain signals (UBS) of EEG. Independent Component Analysis (ICA) is effective to split the signal into independent components (ICs) whose re-projections on 2D scalp topographies (images), also called topoplots, allow to recognize/separate artifacts and by UBS. Until now, IC topoplot analysis, a gold standard in EEG, has been carried on visually by human experts and, hence, not usable in automatic, fast-response EEG. We present a completely automatic and effective framework for EEG artifact recognition by IC topoplots, based on 2D Convolutional Neural Networks (CNNs), capable to divide topoplots in 4 classes: 3 types of artifacts and UBS. The framework setup is described and results are presented, discussed and compared with those obtained by other competitive strategies. Experiments, carried on public EEG datasets, have shown an overall accuracy of above 98%, employing 1.4 sec on a standard PC to classify 32 topoplots, that is to drive an EEG system of 32 sensors. Though not real-time, the proposed framework is efficient enough to be used in fast-response EEG-based Brain-Computer Interfaces (BCI) and faster than other automatic methods based on ICs.
