Dept. Computer Science, University of Otago, Dunedin, New Zealand.
Dept. of Psychology, University of Otago, Dunedin, New Zealand.
J Neurosci Methods. 2017 Nov 1;291:213-220. doi: 10.1016/j.jneumeth.2017.08.031. Epub 2017 Aug 30.
EEG signals are often contaminated with artefacts, particularly with large signals generated by eye blinks. Deletion of artefact can lose valuable data. Current methods of removing the eye blink component to leave residual EEG, such as blind source component removal, require multichannel recording, are computationally intensive, and can alter the original EEG signal.
Here we describe a novel single-channel method using a model based on the ballistic physiological components of the eye blink. This removes the blink component, leaving uncontaminated EEG largely unchanged. Processing time allows its use in real-time applications such as neurofeedback training.
Blink removal had a success rate of over 90% recovered variance of original EEG when removing synthesised eye blink components. Fronto-lateral sites were poorer (∼80%) than most other sites (92-96%), with poor fronto-polar results (67%).
When compared with three popular independent component analysis (ICA) methods, our method was only slightly (1%) better at frontal midline sites but significantly (>20%) better at lateral sites with an overall advantage of ∼10%.
With few recording channels and real-time processing, our method shows clear advantages over ICA for removing eye blinks. It should be particularly suited for use in portable brain-computer-interfaces and in neurofeedback training.
脑电图信号常受到伪迹的干扰,尤其是由眼动引起的大信号。去除伪迹可能会丢失有价值的数据。目前去除眨眼成分以保留残留脑电图的方法,如盲源分量去除,需要多通道记录,计算量很大,并且可能会改变原始脑电图信号。
这里我们描述了一种使用基于眨眼生理弹道模型的新单通道方法。该方法去除眨眼成分,基本保持未受污染的脑电图不变。处理时间允许其在神经反馈训练等实时应用中使用。
当去除合成的眼动眨眼成分时,眨眼去除的成功率超过 90%,恢复了原始脑电图的方差。额侧部位(约 80%)比大多数其他部位(92-96%)差,额极部位的效果差(67%)。
与三种常用的独立成分分析(ICA)方法相比,我们的方法在额中线部位仅略好(1%),但在侧部明显好(>20%),整体优势约为 10%。
本方法使用较少的记录通道和实时处理,在去除眨眼方面明显优于 ICA。它特别适合用于便携式脑机接口和神经反馈训练。