gh concentration, respectively. The time is about 20 minutes. Without any processing, about 1170 groups of attention value (one for every 1026 ms) will be collected, and 50 groups will be randomly extracted for data analysis.

Subjects should not have a history of mental illness or have taken psychiatric drugs and no history of epilepsy and other diseases that have a greater impact on brain fluctuations. On the night before the experiment, they were told to have a good rest and not to drink stimulants the day or the day before the experiment.

For the experimental environment, ensure that it is bright and quiet and try to choose a comfortable environment for the tested personnel. The volunteers were required to close their eyes and rest before starting the test. The EEG data reached a low noise and stable state, and then, the test of the corresponding scene was started at the command of the staff.

The data in [Figure 6](#fig6){ref-type="fig"} is all after big packet optimization, where Attention_Cal is the result of small packet optimization (blink feedback). Obviously, even without small packet optimization, the attention value is still more stable and accurate than the data in [Figure 1](#fig1){ref-type="fig"}. Big packet optimization has eliminated most of the wrong data caused by hardware, microexpressions, and environment, while small packet optimization makes the change of attention more gradual and precise, with almost no abnormal sharp rise or plunge.

[Figure 7](#fig7){ref-type="fig"} shows the attention levels of situations. After grading, it can be seen clearly that the overall attention in study is much higher than that in game. And the grade distribution itself also conforms to the principle of normal distribution, so it can be seen that the results of this algorithm conform to the expected and subjective expectations of the volunteers. No change across two or more levels, or other abnormal changes, can be a stable reflection of the trend of attention value.

6. Conclusion {#sec6}
=============

T