Game Control System Based On Multi-Modal Bioelectric Signals

A brain-computer interface system refers to an interactive system that connects the brain with a computer or other equipment,and realizes contjrol of external equipment by analyzing brain signals.This system can effectively enhance the ability of patients with physical disabilities to control external equipment As the control environment becomes more and more complex,the multi-modal brain-computer interface system has attracted more and more attention from researchers.It can carry out multi-dimensional signal control and has higher control accuracy and system stability.This paper combines multi-modal bioelectrical signals with a game system to study and design a multi-modal bioelectrical signal composed of human ocular electricity,myoelectricity and brain electricity(EEG)signals to coordinately control the game system of characters.The system can provide a good gaming experience for people with physical disabilities.The main research contents of this paper are as follows:(1)This paper designs the Schulte grid paradigm to realize the collection and automatic labeling of EEG data of different types of attention,build a multi-level EEG attention database,and use the classic concentration model to analyze the accuracy of the attention database.Experiments have concluded that this attention grading model can well distinguish the three attention levels of high,medium and low.(2)In the study of attention grading,in view of the technical difficulty that the EEG feature extraction algorithm ignores the timing features of the original EEG signals,a long and short-term memory deep learning network is designed to save the timing features of the original EEG signals.Compare the five existing attention grading algorithms based on EEG signals:wavelet transform,approximate entropy,co-space mode,brain network based on coherence coefficients,and convolutional neural network.On the same EEG data set,this attention grading model.The recognition accuracy rate is the highest,the highest accuracy rate is 92.64%.(3)In order to solve the common individual differences in brain-computer interface systems,this system has designed an adaptive facial expression subsystem.Each subject can design data indicators triggered by game actions according to the comfort level of their facial expressions,thereby improving player comfort Enhance the practicability of the system.(4)Build a complete brain-computer interface game system,use Emotiv Epoc+EEG to collect bioelectric signals,design and implement server-side data processing platform,User Datagram Transmission Protocol(UDP)and King of Fighters game client respectively to complete signal processing,Conversion and transmission,and ultimately control the game process.Experiments show that the subjects operate smoothly in the multi-modal real-time game system,and can control the game characters in real time through multi-modal signals.