EEG data analysis repository for Shalu et al. Similar but different: ERP evidence on the processing of mental and physical experiencer verbs in Malayalam.
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EEG data analysis repository (Version 2) for Shalu et al. Similar but different: ERP evidence on the processing of mental and physicalexperiencer verbs in Malayalam (https://doi.org/10.3389/flang.2025.1599924). The preprocessed data from https://doi.org/10.5281/zenodo.14986232 was imported in R (Version 4.4.2; R Core Team, 2024) using the eeguana package (Version 0.1.11.9001; Nicenboim, 2018) for epoching and statistical analysis. Single trial EEG epochs were used for statistically analysing the mean amplitudes in selected time-windows of interest by fitting linear mixed effects models (LMEM) using the lme4 package (Bates et al., 2015) in R. The names of the zipped folders describe their respective contents. The Code_In_Context_Analysis_Output_R_Notebooks folder contains the R Notebooks of the LMEM analyses of the behavioural and ERP data. These notebooks show the code, data and output in context, and provide full model summaries and details for all the models reported in the article. The R scripts themselves reside in a folder of their own, which also includes the version of the eeguana package we used for the analyses, and the custom-made helper scripts for processing the EEG data, epoching them and extracting mean amplitudes from them. The EEG and Epochs data resulting from the ERP analysis, as well as the single-trial mean amplitudes and prestimulus mean amplitudes extracted for each time-window of interest are in EEG_And_Epochs_Data_Files. Since the LMEM models computed were quite complex, the model objects are provided as RDS files for easily importing them into R without having to compute the models again. The plots generated at various stages of the analysis are in the Plots folder. References Bates, D., Mächler, M., Bolker, B., and Walker, S. (2015). Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1. doi: 10.18637/jss.v067.i01. Nicenboim, B. (2018). eeguana: A package for manipulating EEG data in R. Version 0.1.11.9001. Retrieved from https://github.com/bnicenboim/eeguana. http://doi.org/10.5281/zenodo.2533138. R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org. Shalu, S., Choudhary, K. K., & Muralikrishnan, R. (2025). Dataset with EEG data from Malayalam speakers. (Shalu et al., 2025) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.14986232
本数据集为Shalu等人的脑电数据分析仓库（版本2），对应研究论文《相似却有差异：马拉雅拉姆语心理与物理体验动词加工的事件相关电位（Event-Related Potential, ERP）证据》（https://doi.org/10.3389/flang.2025.1599924）。 本研究从https://doi.org/10.5281/zenodo.14986232获取预处理后的脑电数据，并在R语言（版本4.4.2；R开发核心团队，2024）中借助eeguana包（版本0.1.11.9001；Nicenboim，2018）完成数据分段与统计分析流程。 研究采用单试次脑电分段数据，通过R语言中的lme4包（Bates等人，2015）拟合线性混合效应模型（Linear Mixed Effects Models, LMEM），对选定的感兴趣时间窗口内的平均振幅开展统计分析。 各压缩文件夹的命名与其存储内容一一对应。`Code_In_Context_Analysis_Output_R_Notebooks`文件夹包含行为数据与事件相关电位数据的线性混合效应模型分析R脚本文件（R Notebooks），该文件可同步展示代码、数据与输出结果，并提供论文中报告的所有模型的完整摘要与细节信息。独立文件夹中存储了本次分析使用的全部R脚本，其中包含本次分析所用版本的eeguana包，以及用于脑电数据处理、分段与平均振幅提取的自定义辅助脚本。 ERP分析得到的脑电与分段数据、各感兴趣时间窗口提取的单试次平均振幅及预刺激平均振幅，均存储于`EEG_And_Epochs_Data_Files`文件夹中。由于本次研究构建的线性混合效应模型较为复杂，本仓库提供了模型对象的RDS文件，方便研究者直接导入R语言环境，无需重复计算模型。分析各阶段生成的可视化图表存储于`Plots`文件夹中。 ### 参考文献 1. Bates, D., Mächler, M., Bolker, B., 及 Walker, S. (2015). 使用lme4拟合线性混合效应模型. 《统计软件期刊》, 67, 1. doi: 10.18637/jss.v067.i01. 2. Nicenboim, B. (2018). eeguana：R语言脑电数据处理包. 版本0.1.11.9001. 来源：https://github.com/bnicenboim/eeguana. http://doi.org/10.5281/zenodo.2533138. 3. R开发核心团队 (2024). R：统计计算语言与环境. 奥地利维也纳R统计计算基金会. https://www.R-project.org. 4. Shalu, S., Choudhary, K. K., 及 Muralikrishnan, R. (2025). 马拉雅拉姆语使用者脑电数据集. (Shalu等人, 2025) [数据集]. Zenodo. https://doi.org/10.5281/ZENODO.14986232
