Brief overview of the paper and its main findings

Generally speaking, this is an article about how to distinguish those game users who have little experience in games, those who have certain experience in games but healthy, and users with gaming disorder, so as to distinguish them through a variety of physiological signals and machine learning methods. I think it's a very interesting study, very enlightening to the field of gaming disorder.

Major and minor points

Major: 1. The number of subjects is too small, less than 20 in each group. 2. Group definition, how to define entertainment users, self-report or other comments, so long game time does not affect life? More than 21 hours a week. Are there any professional players? Is it the most popular / fascinating game for each player when choosing materials? 3. There is no multiple comparison correction for multiple indicators. Each statistical test is a test of the possibility and must be corrected. 4. Please take the familiarity of video as a single factor and report its correlation with pretest, posttest, and anteroposterior changes. In order to eliminate the effect of new heterosexual. Minor 1. The statistical report is extremely irregular (italics, degrees of freedom, effect quantity). In terms of statistical use, Fig3, we should report the results of covariance analysis with previous measurements as covariates, so as to compare the relative changes of the three "groups". Is the power of EEG normally distributed? 2. For recall and precision, please use more general terms to facilitate subject communication. 3. Cross validation is only 50% off. 4. The figure is too simple and needs to be detailed. For example, what does Fig3 wash mean? 5. “3.! "Results" is an input error? 6. Other information that may affect the physiological state of the day, such as whether to stay up late the previous day, etc.

Conflicts of interest

I have np conflicts of interest here.