Leach Stephanie C, Morales Santiago, Bowers Maureen E, Buzzell George A, Debnath Ranjan, Beall Daniel, Fox Nathan A
Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA.
Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD, USA.
Psychophysiology. 2020 Aug;57(8):e13566. doi: 10.1111/psyp.13566. Epub 2020 Mar 17.
A major challenge for electroencephalograph (EEG) studies on pediatric populations is that large amounts of data are lost due to artifacts (e.g., movement and blinks). Independent component analysis (ICA) can separate artifactual and neural activity, allowing researchers to remove such artifactual activity and retain a greater percentage of EEG data for analyses. However, manual identification of artifactual components is time-consuming and requires subjective judgment. Automated algorithms, like ADJUST and ICLabel, have been validated on adults, but to our knowledge, no such algorithms have been optimized for pediatric data. Therefore, in an attempt to automate artifact selection for pediatric data collected with geodesic nets, we modified ADJUST's algorithm. Our "adjusted-ADJUST" algorithm was compared to the "original-ADJUST" algorithm and ICLabel in adults, children, and infants on three different performance measures: respective classification agreement with expert coders, the number of trials retained following artifact removal, and the reliability of the EEG signal after preprocessing with each algorithm. Overall, the adjusted-ADJUST algorithm performed better than the original-ADJUST algorithm and no ICA correction with adult and pediatric data. Moreover, in some measures, it performed better than ICLabel for pediatric data. These results indicate that optimizing existing algorithms improves artifact classification and retains more trials, potentially facilitating EEG studies with pediatric populations. Adjusted-ADJUST is freely available under the terms of the GNU General Public License at: https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline/tree/master/adjusted_adjust_scripts.
脑电图（EEG）在儿科人群中的研究面临的一个主要挑战是，由于伪迹（如运动和眨眼）会丢失大量数据。独立成分分析（ICA）可以分离伪迹活动和神经活动，使研究人员能够去除此类伪迹活动，并保留更高比例的EEG数据用于分析。然而，人工识别伪迹成分既耗时又需要主观判断。像ADJUST和ICLabel这样的自动化算法已在成人中得到验证，但据我们所知，尚无此类算法针对儿科数据进行优化。因此，为了尝试对通过测地线网收集的儿科数据实现伪迹选择自动化，我们修改了ADJUST算法。我们将“调整后的ADJUST”算法与“原始ADJUST”算法以及ICLabel在成人、儿童和婴儿中进行了三种不同性能指标的比较：与专家编码员的各自分类一致性、去除伪迹后保留的试验次数，以及每种算法预处理后EEG信号的可靠性。总体而言，调整后的ADJUST算法在成人和儿科数据上的表现优于原始ADJUST算法，且无需进行ICA校正。此外，在某些指标上，它在儿科数据方面的表现优于ICLabel。这些结果表明，优化现有算法可改善伪迹分类并保留更多试验，可能有助于儿科人群的EEG研究。调整后的ADJUST可根据GNU通用公共许可证的条款免费获取，网址为：https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline/tree/master/adjusted_adjust_scripts 。
