Psychological fatigue includes mental fatigue and burnout. In order to investigate the impact of psychological fatigue on athletes' response monitoring, event-related potentials technique is typically used, and the most critical indicator is error-related negativity. Two experiments were carried out to explore cause-effect relationships of psychological fatigue and response monitoring. The event-related potentials data processing was based on Artificial Intelligence computing methods, including wavelet transform, grayscale transformation and other algorithms. The first experiment was done to explain mental fatigue and response monitoring. 15 participants operated continuously 60 min Flanker task, and then operated 15 min task accompanied by light music. From the results of behavioral performances, the fatigue period compared with the fatigue-free period showed significant differences, including reaction time (p = 0.029) and correct rate (p = 0.046). From the results of error-related negativity, the amplitude of the fatigue-free period was bigger than that of the music adjustment period, the latter was bigger than that of the fatigue period (p < 0.001). The second experiment was conducted to explore burnout and response monitoring. Twenty-four participants were separated averagely into two groups. One group was burnout group, the other group was no burnout group. They both operated 15 min task. From the results of behavioral performances, no burnout group was better than burnout group. From the results of error-related negativity, no burnout group’s amplitude was bigger than that of burnout group, but the difference was marginally significant. The conclusions are as follows: Artificial intelligence technology is feasible for processing event-related potentials data. Psychological fatigue weakens athletes’ response monitoring ability, and the effect of mental fatigue is significant. In future researches, the following topics should be concerned, including the mediating or moderating effects of third variables, different ways of recovering from mental fatigue, computer data simulator and date accuracy, brain-computer interfaces and error-related negativity, etc.

心理疲劳包括精神疲劳和倦怠。为了研究心理疲劳对运动员反应监测的影响，通常使用事件相关电位技术，其中最关键的指标是错误相关的消极性。进行了两个实验来探索心理疲劳和反应监测的因果关系。事件相关电位数据处理基于人工智能计算方法，包括小波变换、灰度变换等算法。第一个实验是为了解释精神疲劳和反应监测。15名参与者连续进行60分钟的侧卫任务，然后在轻音乐的伴奏下进行15分钟的任务。从行为表现的结果来看，p  = 0.029）和正确率（p  = 0.046）。从误差相关负性结果来看，无疲劳期的幅度大于音乐调整期的幅度，后者大于疲劳期的幅度（p < 0.001)。第二个实验旨在探索倦怠和反应监测。24 名参与者平均分为两组。一组为倦怠组，另一组为无倦怠组。他们都操作了 15 分钟的任务。从行为表现结果来看，无倦怠组优于倦怠组。从误差相关负性结果来看，无倦怠组的幅度大于倦怠组，但差异有边际显着性。结论如下：人工智能技术对事件相关电位数据的处理是可行的。心理疲劳削弱了运动员的反应监测能力，心理疲劳效果显着。在未来的研究中，应关注以下主题，