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Eye blink correction: a test on the preservation of common ERP components using a regression based technique

机译:眨眼矫正:使用基于回归的技术对通用ERP组件的保存进行测试

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摘要

Eye blinks are a pervasive problem in electroencephalography research as they contaminate the brain signal. This paper tests the merits of a software tool employing the regression-based Gratton method that claims to remove the detrimental effects of the eye blink and leaves the activity of the brain. The efficacy of the correction tool was tested on five common stimulus-locked Event Related Potential (ERP) components used in a standard Go/Nogo task. Results suggested that the ‘corrected’ data could be predicted from data containing no eye blinks, suggesting the tool does not distort the data to great extent. This effect was found significant for all components, except for the P3. The conclusion is that this tool distorts the data at acceptable levels, yet caution should be taken when interpreting later components, like the P3.
机译:眨眼是脑电图研究中普遍存在的问题,因为它们会污染大脑的信号。本文测试了使用基于回归的Gratton方法的软件工具的优点,该方法声称可以消除眨眼的有害影响并保持大脑活动。在标准Go / Nogo任务中使用的五个常见的刺激锁定事件相关电位(ERP)组件上测试了校正工具的功效。结果表明,可以从不眨眼的数据中预测出“校正后”的数据,这表明该工具不会在很大程度上扭曲数据。发现该效果对除P3以外的所有组件均很显着。结论是该工具会使数据失真到可接受的水平,但是在解释后面的组件(例如P3)时应格外小心。

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