The effect of hair type and texture on electroencephalography and event-related potential data quality.
The effect of hair type and texture on electroencephalography and event-related potential data quality.
复制标题
头发类型和质地对脑电图和事件相关电位数据质量的影响。
DOI：
10.1111/psyp.14499
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发表时间：
2024
期刊：
影响因子：
3.7
通讯作者：
Gatzke-Kopp,LisaM
中科院分区：
文献类型：
--
作者：
Lees,Ty;Ram,Nilam;Swingler,MargaretM;Gatzke-Kopp,LisaM
利用事件相关电位（ERP）方法的研究在样本代表性方面通常存在偏差。造成样本偏差的众多因素之一是研究人员对头发质地、体积和款式的种族差异影响电极放置以及随后的研究资格的程度的假设。当前的研究使用从 n = 213 名 17-19 岁个体收集的数据来检验这些影响，并为收集所有发质类型的 ERP 数据提供指导。使用视觉量表量化头发质地的个体差异，并通过测量帽子制备中使用的凝胶的量来量化头发体积的个体差异。在预处理、后处理和变量生成阶段使用多种指标评估脑电图数据质量。结果表明，头发体积与信号质量和信号幅度的微小但系统性差异相关。这种差异是非常有问题的，因为它们可能被错误地归因于群体之间的认知差异。然而，将凝胶体积作为协变量来解释头发体积的个体差异显着减少了，在大多数情况下消除了群体差异。我们讨论了克服实际和感知的技术障碍的策略，帮助研究人员寻求在 ERP 研究中实现更大的包容性和代表性。
Research utilizing event‐related potential (ERP) methods is generally biased with regard to sample representativeness. Among the myriad of factors that contribute to sample bias are researchers' assumptions about the extent to which racial differences in hair texture, volume, and style impact electrode placement, and subsequently, study eligibility. The current study examines these impacts using data collected fromn= 213 individuals ages 17–19 years, and offers guidance on collection of ERP data across the full spectrum of hair types. Individual differences were quantified for hair texture using a visual scale, and for hair volume by measuring the amount of gel used in cap preparation. Electroencephalography data quality was assessed with multiple metrics at the preprocessing, post‐processing, and variable generation stages. Results indicate that hair volume is associated with small, but systematic differences in signal quality and signal amplitude. Such differences are highly problematic as they could be misattributed to cognitive differences among groups. However, inclusion of gel volume as a covariate to account for individual differences in hair volume significantly reduced, and in most cases eliminated, group differences. We discuss strategies for overcoming real and perceived technical barriers for researchers seeking to achieve greater inclusivity and representativeness in ERP research.