Research On Control Of Video Game System Based On Bimodal Bioelectrical Signal Control

With the improvement of people's pursuit of spiritual culture and entertainment,video games have become the choice of more people,which also makes the game industry have higher requirements for the upgrade of game types.With the development of science and technology,many enterprises combine traditional games with emerging technologies to develop functional games.The game upgrade brings players a richer game experience.In this thesis,we try to combine human bioelectrical signals with vedio games,and study and design an electronic game system that uses the subject 's electroencephalogram signals and Electrooculogram signals to control the relevant actions of the game characters.Being able to train teenagers(especially those with ADHD)for concentration can also help to restore the brain power of patients with cerebral palsy.The main contents and innovations of this thesis are as follows:(1)The wavelet packet algorithm is used to extract the energy characteristics of the rhythm signal of the human body in different mental states,and the concentration model Focus is established according to the sensitivity index selected by the ReliefF algorithm.Compared with the existing two concentration models F1=E?/E? and F2=(Ea+Ep)/Ee,the Pearson correlation coefficient between Focus model and the original EEG signal is 0.6017,showing a strongly positive correlation.Using the one-way variance theory,the three concentration models have significant differences in different mental states,and the change trend of Focus trend more meets the control needs of concentration in the game system of this thesis.In order to verify the accuracy of the model built,this thesis uses the fuzzy C-means method to classify and recognize the different mental states of the subjects,with an average recognition accuracy rate of 89.06%.Therefore,the model Focus can be used as an evaluation index of the human body's concentration state.(2)A detection method for intentional blinking is proposed.Based on the analysis of the electrooculogram signal,the blink intensity value MSG_BLINK output by the TGAM module is used as the effective data of the blink signal,and the threshold detection is used to effectively recognize the intentional blink of human body,and the intentional blink is used to control the jumping action of the game character.(3)In this thesis,we use E4A platform to build a video game system and develop a game application program based on Android system.Through the test of 10 subjects on the video game system,the experiment shows that:The game system can complete the cooperative control of the game action by the EEG signals and EOG signals,among which the accuracy of the intentional blink detection algorithm in the game system can reach 88.25%,and the subjects have a good game experience.