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脑电图中眨眼伪迹的校正:两种主要方法的比较。

The correction of eye blink artefacts in the EEG: a comparison of two prominent methods.

作者信息

Hoffmann Sven, Falkenstein Michael

机构信息

Leibniz Research Centre for Working Environment and Human Factors (IfADo), Project Group Ageing and CNS-alterations, Dortmund, Germany.

出版信息

PLoS One. 2008 Aug 20;3(8):e3004. doi: 10.1371/journal.pone.0003004.

Abstract

BACKGROUND

The study investigated the residual impact of eyeblinks on the electroencephalogram (EEG) after application of different correction procedures, namely a regression method (eye movement correction procedure, EMCP) and a component based method (Independent Component Analysis, ICA).

METHODOLOGY/PRINCIPLE FINDINGS: Real and simulated data were investigated with respect to blink-related potentials and the residual mutual information of uncorrected vertical electrooculogram (EOG) and corrected EEG, which is a measure of residual EOG contribution to the EEG. The results reveal an occipital positivity that peaks at about 250 ms after the maximum blink excursion following application of either correction procedure. This positivity was not observable in the simulated data. Mutual information of vertical EOG and EEG depended on the applied regression procedure. In addition, different correction results were obtained for real and simulated data. ICA yielded almost perfect correction in all conditions. However, under certain conditions EMCP yielded comparable results to the ICA approach.

CONCLUSION

In conclusion, for EMCP the quality of correction depended on the EMCP variant used and the structure of the data, whereas ICA always yielded almost perfect correction. However, its disadvantage is the much more complex data processing, and that it requires a suitable amount of data.

摘要

背景

本研究调查了应用不同校正程序后眨眼对脑电图(EEG)的残留影响,即回归方法(眼动校正程序,EMCP)和基于成分的方法(独立成分分析,ICA)。

方法/主要发现:研究了真实数据和模拟数据的眨眼相关电位以及未校正的垂直眼电图(EOG)与校正后的EEG之间的残留互信息,残留互信息是衡量EOG对EEG残留贡献的指标。结果显示,应用任一校正程序后,在最大眨眼偏移后约250毫秒出现枕叶正电位峰值。在模拟数据中未观察到这种正电位。垂直EOG和EEG的互信息取决于应用的回归程序。此外,真实数据和模拟数据获得了不同的校正结果。ICA在所有条件下都能实现几乎完美的校正。然而,在某些条件下,EMCP产生的结果与ICA方法相当。

结论

总之,对于EMCP,校正质量取决于所使用的EMCP变体和数据结构,而ICA始终能实现几乎完美的校正。然而,其缺点是数据处理要复杂得多,并且需要适量的数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8cb/2500159/be59d7a09eba/pone.0003004.g001.jpg

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