Using EEG data as Dynamic Difficulty Adjustment in a serious game about the plastic pollution in the oceans

In this work, it is explored whether real-time EEG (Electroencephalography) can adjust the difficulty in a serious game focused on engagement, attention, and learning about plastic pollution in our oceans. Using EEG to balance the game around the players’ affective state by measuring brain activity in real time, it is aimed to better fit the player’s skill level, enabling a stable flow state. The experimental study included 34 participants with an experimental group (n=17), and a control group (n=17). The experimental group played the game about the plastic pollution in our oceans with an
adaptive difficulty adjustment (DDA) based on changes in their levels of attention and calm measured by EEG. The evaluation is based on a user engagement questionnaire, structured interviews, the EEG data, and a knowledge test. The results revealed high engagement in the game from both the experimental group and the control group. However, the participants in the control group were more attentive while playing the game and scored higher on all questions in the knowledge test compared to the experimental group. In conclusion, our study cannot provide evidence for using
EEG-DDA to increase the engagement, attention, and learnings about pollution in the oceans in a serious game. However, there are still advantages for including EEG in game related research, and much future research is needed in how to provide optimal learning in serious games.