基于动作类电子游戏不同水平人群的脑电beta节律频数分析法

doi:10.3969/j.issn.0258-8021.2021.05.08

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Abstract Action video games (AVGs) have become one of the most prominent entertainment in contemporary life. Recently, many studies have shown that the action video games have important impacts on player′s cognitive functions such as attention, which is related to EEG beta rhythm. However, traditional EEG rhythm analysis is based on the average of power strength across temporal segments, which may be difficult to continuously assess the potential effects of AVG levels on brain function. Therefore, based on machine learning, we proposed a new EEG rhythm ratio analysis method. This study included 232 healthy League of Legends male players, and the game levels were divided into level 1 to level 5 (from low to high, corresponding to 27, 74, 77, 34 and 20 subjects) according to the expertise ranking. The game state and resting state EEG for each subject were both recorded. The proposed rhythm ratio analysis contained following steps: 1) difference values of beta rhythm power between game state and resting state were calculated and used as features; 2) all features of data segments for each subject would be relabeled using the support vector machine with radial basis function kernel (high-level/low-level beta pattern), while the training datasets were level 1 and level 5 groups (others were test dataset), and the ratios of predicted high-level beta patterns were obtained for each subject; and 3) finally, the effect of game level on the ratio of high-level beta patterns was assessed using one-way ANOVA, and Pearson′s correlation between game performance score and the ratio of high-level beta patterns was investigated. The results of one-way ANOVA depicted that there were significant differences among the different game level groups (P<0.05, F=17.40), and the ratio of high-level beta patterns was increasing with the increase of game level. There were also significant differences among the different game level groups using ratio analysis based on clustering (P<0.05,F=6.57) and linear discriminant (P<0.05,F=14.84) methods. Furthermore, there was a significant correlation between the game performance score and the ratio of high-level beta patterns (P<0.05, r=0.22). It is implied that the potential effects of action video game on brain function, such as attention, could be assessed by the proposed method. The results may provide a new sight to improve our understanding about brain plasticity related to AVGs.

Key words： action video game electroencephalogram beta rhythm machine learning ratio analysis

Received: 20 June 2020

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R318

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	Articles by authors
	Liu Xiaobo
	Zhao Lingling
	Li Yi
	Gong Diankun
	Yao Dezhong
	Dong Li

Cite this article:

Liu Xiaobo,Zhao Lingling,Li Yi, et al. EEG Beta Rhythm Ratio Analysis for Action Video Game Players with Different Game Levels[J]. Chinese Journal of Biomedical Engineering, 2021, 40(5): 574-581.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2021.05.08 OR http://cjbme.csbme.org/EN/Y2021/V40/I5/574

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