Department of Psychology, University of Southern California, USA.
Department of Psychology, University of Southern California, USA.
Dev Cogn Neurosci. 2024 Dec;70:101458. doi: 10.1016/j.dcn.2024.101458. Epub 2024 Sep 28.
EEG studies play a crucial role in enhancing our understanding of brain development across the lifespan. The increasing clinical and policy implications of EEG research underscore the importance of utilizing reliable EEG measures and increasing the reproducibility of EEG studies. However, important data characteristics like reliability, effect sizes, and data quality metrics are often underreported in pediatric EEG studies. This gap in reporting could stem from the lack of accessible computational tools for quantifying these metrics for EEG data. To help address the lack of reporting, we developed a toolbox that facilitates the estimation of internal consistency reliability, effect size, and standardized measurement error with user-friendly software that facilitates both computing and interpreting these measures. In addition, our tool provides subsampled reliability and effect size in increasing numbers of trials. These estimates offer insights into the number of trials needed for detecting significant effects and reliable measures, informing the minimum number of trial thresholds for the inclusion of participants in individual difference analyses and the optimal trial number for future study designs. Importantly, our toolbox is integrated into commonly used preprocessing pipelines to increase the estimation and reporting of data quality metrics in developmental neuroscience.
脑电图研究在增强我们对整个生命周期中大脑发育的理解方面发挥着至关重要的作用。脑电图研究日益增加的临床和政策意义突显了利用可靠的脑电图测量和提高脑电图研究的可重复性的重要性。然而，在儿科脑电图研究中，重要的数据特征，如可靠性、效应大小和数据质量指标，往往报告不足。这种报告的差距可能源于缺乏用于量化脑电图数据这些指标的可访问计算工具。为了帮助解决报告不足的问题，我们开发了一个工具包，该工具包使用用户友好的软件来方便地估计内部一致性可靠性、效应大小和标准化测量误差，从而促进这些措施的计算和解释。此外，我们的工具还提供了在增加的试验次数中进行的子样本可靠性和效应大小估计。这些估计提供了关于检测显著效果和可靠测量所需的试验次数的见解，为个体差异分析中参与者纳入的最小试验次数阈值和未来研究设计的最佳试验次数提供了信息。重要的是，我们的工具包集成到常用的预处理管道中，以增加发育神经科学中数据质量指标的估计和报告。
