Comparison of EEG biofeedback and visual search strategies during e-sports play according to skill level
Bibliographic record
Abstract
About 83% of the perceptual information humans receive from the outside world comes through the visual domain, making the tracking of visual information important for the superior performance of e-sports players.In addition, it is known that e-sports players' emotional state during performance affects their results.However, few studies have examined visual search strategies and electroencephalogram (EEG) findings of e-sports players while engaged in e-sports.Therefore, the present study aimed to investigate the characteristics of visual search activity and EEG feedback during first person shooter (FPS) game play, in which the tracking of visual information is important, and to examine the characteristics of different gaming skill levels.Four skilled and five semi-skilled e-sports participants (mean age ± 19.11, SD = 0.99) participated in this study.Gazepoint GP3 (Canada) was used to measure eye movements, and the Sports KANSEI (Littlesoftware Inc., Japan) was used to analyse the emotional state of the participants by using EEG data.The results showed that the skilled e-sports players checked the camera significantly more frequently than the semi-skilled participants.The results of the area of interest (AOI) analysis showed that skilled e-sports players glanced at the friendly team information displayed at the top of the game screen more often than semi-skilled players.Furthermore, it was found that skilled e-sports players had significantly higher average EEG relaxation values during gameplay than semi-skilled players.Thus, the visual search strategy and emotional state while playing e-sports differed according to gaming skill level.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".