EEG Data for Cognitive Performance in Fast and Slow-Paced Games

This dataset contains electroencephalography (EEG) recordings from 24 human participants (N=24) engaged in two distinct video game pacing conditions: a Fast-Paced Game and a Slow-Paced Game. The study aimed to investigate neurophysiological responses and cognitive load associated with different game speeds. Data Structure The dataset is organized into 24 participant folders (Subj1-Subj24). Within each participant folder, there are two subfolders: "Fast" and "Slow", corresponding to the game pacing conditions. Each of these "Fast" and "Slow" folders contains the five described file types for that specific session. For each participant and game pacing condition (Fast and Slow), the following five types of files are provided: EEG Recording Data (`.edf`): Raw continuous EEG data from the EMOTIV EPOCX system. Recording Metadata (`.json`): JSON-formatted files containing experimental parameters, recording settings, interval markers, and participant details (anonymized where necessary) for each session. EEG Markdown File (`.md.edf`): Markdown files providing additional details, annotations, or pre-processing notes specific to the `.edf` recording. Interval Marker Data (`.csv`): Comma-separated values files detailing event markers or temporal intervals within the EEG recordings, crucial for epoching and event-related potential (ERP) analysis. SAM Questionnaire Survey Data (`.csv`): Self-Assessment Manikin (SAM) questionnaire data collected from participants, providing subjective ratings of arousal and valence related to their experience in each game. Potential Uses This dataset can be valuable for researchers in neuroscience, psychology, human-computer interaction, and game studies interested in:Analyzing neural correlates of cognitive load, attention, and engagement in dynamic environments. Investigating the impact of game design parameters (e.g., pacing) on brain activity. Developing and testing signal processing techniques for EEG data. Correlating subjective affective states (SAM scores) with objective neurophysiological measures. Funding This research is funded by Universitas Bina Nusantara through the 2025 Initiative Project, Code Project PP0030097 and under participation code 097/Proyek.Inisiatif/I/2025 License Creative Commons Attribution 4.0 International (CC BY 4.0).