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

Zhou, Dong-Dong 1 ; Li, Hong-Zhi1; Yang, Jia-Jia2; Lv, Zhen1; Wan, Li-Yang1; Wang, Wo1; Kuang, Li3

Published Sep 06, 2025 on Dryad. https://doi.org/10.5061/dryad.zpc866th0

Data files

Sep 06, 2025 version files 40.84 GB

Data_and_codes.zip

40.84 GB
README.md

2.90 KB
README.txt

3.51 KB

Abstract

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.

Access this dataset on Dryad: 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/.

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) 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.

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) 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.