Automated Artifact Rejection Algorithms Harm P3 Speller Brain-Computer Interface Performance.
Automated Artifact Rejection Algorithms Harm P3 Speller Brain-Computer Interface Performance.
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DOI：
10.1080/2326263x.2020.1734401
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Huggins JE
中科院分区：
文献类型：
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作者：
Thompson DE;Mowla MR;Dhuyvetter KJ;Tillman JW;Huggins JE
脑机接口(BCI)已被用于恢复严重瘫痪患者的交流和控制。然而，基于脑电(EEG)的非侵入性BCI特别容易受到噪声伪影的影响。这些伪像，包括眼电(EOG)，可能比要检测的信号大几个数量级。已经提出了许多自动化方法来去除脑电记录中的EOG和其他伪影，其中大多数基于盲源分离。这项工作提供了十种不同的自动伪像去除方法的性能比较。不幸的是，所有测试的方法都大大降低了P3拼写BCI的成绩，而且所有的方法都更有可能降低成绩，而不是提高成绩。危害最小的方法是SOBI、Jader和EFICA，但即使是这些方法也会导致BCI准确度平均下降约10个百分点。提出了这种经验性能推论的可能机制原因。
Brain-Computer Interfaces (BCIs) have been used to restore communication and control to people with severe paralysis. However, non-invasive BCIs based on electroencephalogram (EEG) are particularly vulnerable to noise artifacts. These artifacts, including electro-oculogram (EOG), can be orders of magnitude larger than the signal to be detected. Many automated methods have been proposed to remove EOG and other artifacts from EEG recordings, most based on blind source separation. This work presents a performance comparison of ten different automated artifact removal methods. Unfortunately, all tested methods substantially and significantly reduced P3 Speller BCI performance, and all methods were more likely to reduce performance than increase it. The least harmful methods were titled SOBI, JADER, and EFICA, but even these methods caused an average of approximately ten percentage points drop in BCI accuracy. Possible mechanistic causes for this empirical performance deduction are proposed.