优秀跳水运动员赛前脑功能训练适应状态的量化分析

发布时间：2019-06-03 18:00

【摘要】：目的:了解跳水运动员赛前运动训练适应状态的特点,探讨其神经生理学特征。方法:利用高级运动训练状态监控仪采集26名备战第12届全国运动会的跳水运动员赛前不同阶段90个样本的脑电(EEG)α波的功率谱值,同时采集其训练计划完成情况等相关数据,采用自组织特征映射(SOM)神经网络聚类分析的方法对其训练适应状态进行分类,并对不同状态运动员的相关指标进行统计分析。结果:优秀跳水运动员的脑电α波功率谱主要集中于10 Hz,国际健将与健将之间的功率值无显著性差异;脑信息熵的变化值与运动成绩的变化值呈中等强度负相关关系(P0.05);状态一般运动员赛前8 Hz水平显著高于状态优秀运动员(P0.05),而10 Hz水平显著低于状态优秀运动员(P0.05)。结论:国际健将与健将之间脑电α波序参量水平差异不显著,无法直接辨别出适应状态的差异;通过脑信息熵的变化可以推测运动员一段时间的运动成绩,即熵值减小,则运动表现更好;SOM聚类分析方法可以将不同训练适应状态的运动员进行分类。
[Abstract]:Objective: to understand the adaptive state of sports training of divers before competition and to explore its neurophysiological characteristics. Methods: the power spectrum values of (EEG) 伪 wave in 90 samples of 26 divers preparing for the 12th National Games before the competition were collected by using the advanced sports training state monitor. At the same time, the relevant data such as the completion of the training plan are collected, and the self-organizing feature mapping (SOM) neural network clustering analysis method is used to classify the training adaptation state, and the related indexes of athletes in different states are statistically analyzed. Results: the EEG 伪 wave power spectrum of excellent divers mainly focused on the power value of 10 Hz, international athletes and athletes. There was a moderate negative correlation between the change of brain information entropy and the change of motor performance (P 0.05). The level of 8 Hz before the competition of the general athletes was significantly higher than that of the elite athletes (P 0.05), while the level of 10 Hz was significantly lower than that of the elite athletes (P 0.05). Conclusion: there is no significant difference in the level of EEG 伪 wave sequence parameters between international athletes and athletes, and the difference of adaptive state can not be directly distinguished. Through the change of brain information entropy, we can infer the athletes' sports performance for a period of time, that is, if the entropy value decreases, the sports performance is better, and the SOM cluster analysis method can classify the athletes with different training and adaptation states.
【作者单位】：华南师范大学体育科学学院;广东省体育局科教宣传与交流处;广东省二沙体育训练中心;
【基金】：广东省体育局重大科研项目(GDSS13001)
【分类号】：R87

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