DOI: 10.5061/dryad.zpc866th0
title: Data from: Neural responses to kills/deaths in real MOBA games are associated with addiction-related traits and subjective pleasant/unpleasant experiences

description: Players of Multiplayer Online Battle Arena (MOBA) games are at a
 heightened risk of developing Internet Gaming Disorder (IGD). We aimed to
 investigate the neural responses triggered by kills and deaths during real
 MOBA gameplay and explore their association with addiction-related
 psychological traits and subjective pleasant or unpleasant experiences. We
 developed an experimental protocol to capture moments of kills and deaths
 during real MOBA gameplay. Game players who frequently play "Honor of
 Kings" or "League of Legends" on mobile phones for at least
 12 months were recruited. All participants completed the IGD-20 assessment
 and played six matches while concurrently recording electroencephalography
 with a 64-channel setting. Among males, the amplitude of P300 induced by
 kills and deaths showed a significant negative correlation with IGD-20
 scores. Additionally, the post-death beta-band event-related
 synchronization (ERS) and theta-band event-related desynchronization (ERD)
 were significantly positively correlated with IGD-20 scores in males,
 while no significant correlation was found among females. The amplitude of
 P300 following deaths was significantly negatively correlated with
 subjective unpleasant experiences in males, yet positively correlated in
 females. Regardless of gender, the amplitude of post-kill P300 and
 beta-ERS were negatively correlated with pleasant experiences. This study
 establishes an ecologically-embedded paradigm that successfully captures
 real-time neural signatures of valenced in-game events in MOBA games. We
 offer a novel methodological framework and theoretical perspective for
 investigating IGD and will inspire further research into the neural
 mechanisms underlying IGD in naturalistic gaming environments.
description: # Data from: Neural responses to kills/deaths in real MOBA games are
 associated with addiction-related traits and subjective
 pleasant/unpleasant experiences **Access this dataset on Dryad:**
 [https://doi.org/10.5061/dryad.zpc866th0](https://doi.org/10.5061/dryad.zpc866th0) This dataset contains the data and code used in the published article: [https://pubmed.ncbi.nlm.nih.gov/40818061/](https://pubmed.ncbi.nlm.nih.gov/40818061/). ## Description of the data and file structure * 1\. The non_ST folder contains all the data and code for non-single-trial analyses. 'general_characteristics.xlsx' includes the general characteristics of the enrolled participants, such as age, gender, and Internet Gaming Disorder 20-item (IGD-20) scores. sub_**.fdt and sub_**.set files are the EEG data for each participant. These files can be opened using EEGLAB (an open-source MATLAB toolbox available at: [https://github.com/sccn/eeglab](https://github.com/sccn/eeglab)) or MNE-Python (an open-source Python package). The TF_analysis folder contains all time-frequency analysis results and the ERP_analysis folder contains all event-related potential analysis results. * 2\. The ST folder contains all the data and code for single-trial analyses. sub_**.fdt and sub_**.set files are the EEG data for each participant. These files can be opened using EEGLAB (an open-source MATLAB toolbox available at: [https://github.com/sccn/eeglab](https://github.com/sccn/eeglab)) or MNE-Python (an open-source Python package). The TF_analysis folder contains all time-frequency analysis results and the ERP_analysis folder contains all event-related potential analysis results. * 3\. We have kept the NaN values in the data (if they exist). This is because NaN is the standard identifier for 'Not a Number' in MATLAB, which is the primary environment for our data processing and analysis pipeline. ## Sharing information Due to copyright restrictions, some MATLAB plugins and functions are not included in this dataset. However, they can be requested from the corresponding author ([zhoudongdong@cqmu.edu.cn](mailto:zhoudongdong@cqmu.edu.cn)) for legitimate research purposes. ## Codes All relevant code has been included in this dataset. ## Human subjects data We confirm that we obtained explicit written consent from all participants to publish de-identified data in the public domain. To ensure participant anonymity, we implemented the following de-identification measures: Removed all direct identifiers (e.g., names, addresses, phone numbers, email addresses) from the dataset; Replaced original participant identifiers with anonymized subject codes (e.g., sub_01, sub_02); Excluded precise geographical information and other indirect identifiers that could lead to participant re-identification; Ensured that no combination of variables retained in the dataset could uniquely identify any individual.