An Introduction to EEG Analysis Techniques and Brain-Computer Interfaces for Games User Researchers
Bibliographic record
Abstract
Games User Research (GUR) can provide meaningful insights into the study of games. As a part of GUR, we focus on the area of cognitive psychology and discuss electroencephalography (EEG) as an evaluation technique for games. We want to introduce game researchers to EEG when studying the cognitive side of player experience and discuss how it can benefit game studies. In this paper, we review EEG techniques before providing researchers with information about general EEG setup and methodology, EEG data collection, preparation, and analysis. Techniques reviewed have been used in medical applications, research, brain-computer interaction (BCI) and human-computer interaction (HCI) applications. In addition, future ideas for applications of EEG techniques in game studies are discussed. We outline how to use different EEG analysis techniques for game research and it is our hope to make these techniques more understandable for the game studies community and to demonstrate their merit for games user research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".