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Updated: Jan 24, 2026

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
Published on: December 16, 2010
The high-working load states induced by action real-time strategy gaming: An EEG power spectrum and network study
Diankun Gong1, Yi Li1, Yuening Yan1
1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Abstract:
Action Real-time Strategy Gaming (ARSG) is a cognitively demanding task that requires attention, sensorimotor skills, high-level team coordination, and strategy-making abilities. Thus, ARSG can offer important, new insights into learning-related neural plasticity. However, little research has examined how the brain allocates cognitive resources in ARSG. By analyzing power spectrums and electroencephalograph (EEG) functional connectivity (FC) networks, this study compared multiple conditions (resting, movie watching, ARSG, and Life simulation gaming - LSG) in two experiments. Consistent with previous research, we found that brain waves appeared to be de-assimilated after activation. Furthermore, results showed that ARSG was associated with higher activation and workload as indicated by θ-waves, and required higher attention as reflected by β-waves. Furthermore, as participants began ARSG, the allocation of cognitive resource gradually prioritized the frontal area, which controls attention, decision-making, monitoring, and mnemonic processing, while participants also showed an enhanced ability to process information under the ARSG condition as indicated by network characteristics. These electrophysiological changes observed in ARSG were not found under LSG. Thus, this study applied both power spectrum and EEG FC networks analyses to ARSG research, revealing characteristics of brain waves in typical areas and how the brain gradually changes from low-working load states to high-working load states based on real-time EEG recordings.
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