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Spatial Quantification of Facial Electromyography Artifacts in the Electroencephalogram

机译:脑电图中面部肌动画厂的空间量化

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The Electroencephalogram (EEG) has been the most preferred way of recording the brain activity due to its noninvasiveness and affordability benefits. Information estimated from EEG has been employed broadly, e.g., for diagnosis or as input signal to Brain Computer Interfaces (BCI). Nevertheless, the EEG is prone to artifacts including non-brain physiological activities, such as eye blinking and the contraction of the muscles of the scalp. Some applications such as BCI systems may occasionally be associated with frequent contractions of muscles of the head corrupting the EEG-based control signal. This requires the application of a number of filtering techniques. However, standard gold techniques for signal filtering still contain limitations, such as the incapacity of eliminating noise in all EEG channels. For this reason, besides studying and applying filtering techniques, it is necessary to understand the contamination from electromyogram (EMG) along the scalp. Several studies concluded that EMG artifact contaminates the EEG at frequencies beginning at 15 Hz on the topographic distribution of the energy that encompasses practically the entire scalp. Thus, the present work aims to quantitatively estimate EMG noise in 16 bipolar channels of EEG distributed along the scalp according to the 10-20 system. This estimation was based on an experimental protocol considering the simultaneous acquisition of EEG and EMG of five facial muscles sampled at 5 kHz. The protocol consisted in activating facial muscles while listening to 15 beep sounds. The evaluated muscles were occipitofrontalis (venter frontalis), masseter, temporalis, zygomaticus major, orbicularis oculi and orbicularis oris. The mean power of the EEG contaminated by EMG of facial muscles contractions was compared between the periods of muscle contraction and non-contraction. The results show that occipitofrontalis and masseter muscular contamination is present over the scalp with increase from 63.5 uV to 816 uV and from 118.3 uV to 5,617.9 uV. respectively.
机译:脑电图(EEG)是由于其非侵入性和负担能力而记录大脑活动的最优选方式。从EEG估计的信息已经广泛使用,例如,用于诊断或作为脑计算机接口的输入信号(BCI)。然而,脑电图易于包括非脑生理活性的工件,例如眼睛闪烁和头皮肌肉的收缩。诸如BCI系统的一些应用偶尔可能与损坏基于EEG的控制信号的头部的频繁收缩相关联。这需要应用许多过滤技术。然而,用于信号滤波的标准金技术仍然包含限制,例如消除所有脑电图信道中的噪声的无干扰。因此,除了研究和应用过滤技术之外,有必要了解沿头皮的电灰度(EMG)的污染。几项研究得出结论,EMG伪影以15 Hz开始于15 Hz的频率污染脑电图,这是整个头皮几乎包含的能量的地形分布。因此,本工作旨在根据10-20系统定量估计沿着头皮分布的16个双极通道中的EMG噪声。本估计基于实验方案,考虑到同时采购脑电图和5 kHz采样的五个面部肌肉的EEG和EMG。该协议在收听15嘟嘟声的同时激活面部肌肉。评估的肌肉是枕骨(venter prontralis),masseter,internalis,zygomatorus major,orbicularis oculi和orbicularis oris。在肌肉收缩期和非收缩期间比较了受面部肌肉收缩的EG污染的脑电图的平均力。结果表明,在头皮上以63.5 uV至816紫外线和118.3 uV至5,617.9紫外线增加,枕骨植物和肌肉肌肉污染物的存在。分别。

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