EEG data analysis repository for shalu et al. Similar does not mean same: ERP correlates of processing mental and physical experiencer verbs in Malayalam complex constructions

EEG data analysis repository for Shalu et al. Similar does not mean the same: ERP correlates of processing mental and physical experiencer verbs in Malayalam complex constructions https://doi.org/10.3389/fnhum.2025.1632844. 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等人为其相关研究构建的脑电数据分析仓库，对应研究论文《相似并不等于相同：马拉雅拉姆语复杂结构中心理与物理体验动词加工的事件相关电位（Event-Related Potential, ERP）关联》，DOI：10.3389/fnhum.2025.1632844。本研究所用的预处理脑电数据取自https://doi.org/10.5281/zenodo.14986232，通过R软件（版本4.4.2；R核心团队，2024）结合eeguana包（版本0.1.11.9001；Nicenboim，2018）完成数据导入、试次分段与统计分析。本研究采用在R环境中借助lme4包（Bates等，2015）拟合的线性混合效应模型（Linear Mixed Effects Model, LMEM），对选定感兴趣时间窗内的平均波幅进行统计分析，分析所用数据为单试次脑电分段数据。 各压缩文件夹的名称均清晰标注其包含的内容。`Code_In_Context_Analysis_Output_R_Notebooks` 文件夹存储了行为数据与ERP数据的LMEM分析R笔记本，该笔记可完整呈现代码、数据与对应输出，并提供论文中报告的全部模型的完整摘要与细节信息。R脚本本体存放在专属文件夹中，该文件夹同时包含本次分析所用版本的eeguana包，以及用于脑电数据处理、试次分段与平均波幅提取的自定义辅助脚本。ERP分析得到的脑电与试次分段数据、各感兴趣时间窗提取的单试次平均波幅与试前平均波幅，均存放于`EEG_And_Epochs_Data_Files`文件夹中。鉴于本次构建的LMEM模型较为复杂，模型对象以RDS格式文件提供，可直接导入R环境无需重新拟合模型。分析各阶段生成的可视化图表存放在`Plots`文件夹中。 参考文献 1. 贝茨, D., 麦克勒, M., 博尔克, B., & 沃克, S. (2015). 基于lme4包拟合线性混合效应模型. 《统计软件杂志》, 67, 1. DOI: 10.18637/jss.v067.i01. 2. 尼森博伊姆, B. (2018). eeguana：用于R环境中脑电数据处理的工具包. 版本0.1.11.9001. 检索自https://github.com/bnicenboim/eeguana. http://doi.org/10.5281/zenodo.2533138. 3. R核心团队 (2024). R：用于统计计算的语言与环境. 奥地利维也纳：R统计计算基金会. https://www.R-project.org. 4. 沙鲁, S., 乔杜里, K. K., & 穆拉利克里希南, R. (2025). 马拉雅拉姆语使用者脑电数据集. [数据集]. Zenodo. https://doi.org/10.5281/ZENODO.14986232.