Research On The Correlation Between Task Motion Behavior And EEG Signals

The movement of human behavior is controlled by the brain,that generates EEG signals pass the central nervous system and peripheral nervous system,transmit to the muscle tissue,and the neural signals stimulate muscle contraction and relaxation to complete the corresponding action.The cerebral cortex is the most advanced center of human movement posture control,and the EEG signal is the electrical activity of the brain.The studying of relationship between the complex human task behavior and EEG signals,the exploring of EEG and human cognitive behavioral relationship between forward and reverse and to decode time and space relationship between task behavior pattern and EEG signals,are important theoretical significance and practical value for the development of clinical diagnosis and rehabilitation treatment of cerebral disease,humanoid robot field.The golf putting behavior and driving task behavior are as the research objects in this paper.Then the golf players and the drivers movement data,posture information and EEG signals will be real-time collected.The correlation between human task behavior and EEG signals should be analyzed.Human motion capture and motion analysis technology play an increasingly important role in athletes simulation training,medical rehabilitation,humanoid robot and 3D animation.The motion capture and recognition are difficult because of the complexity and diversity of human motion.Therefore,it is an urgent problem that which methods of motion capture is used to study and analyze human motion behavior.So the wireless inertial motion capture technology will be used to capture motion parameters of the human body,collect motion parameters of the limb joints.The 3D human motion attitude model is constructed,the movement joint space trajectory is planed,and limb motion process will be reproducted.Secondly,golf putting and driving motion experiments will be set up.The motion capture using inertial motion capture module,somatosensory motion capture equipment and EEG signal acquisition device will be introduced to get the clubbing movement parameters,arm movement posture and EEG signal characteristics.Then,the analysis of relationship between EEG signals and human motion is presented.Finally,this paper focuses on the analysis of the relationship between the golf putting task behavior,the turning left and right task of the joint motion information and the characteristics of the subjects EEG signals.The characteristics of event-related EEG signals are extracted,the spatio-temporal patterns of EEG signal characteristics and task behavior are established,and the potential performance of brain nerve feedback under the action of the task is decoded.The main researches of this paper are as follows:1.In view of the singularity problem of human body motion capture in the attitude algorithm,this paper presents a Dual-Quaternions method for human motion attitude calculation.The motion capture system of wireless inertial technology is used to capture the human motion attitude information in real-time,and the motion parameters of the human body are obtained under different motion postures.The attitude calculation method can describe the action process of the body and to simulate the trajectory of joints,using OpenGL image processing method and ADAMS humanoid robot model.The human rod model and 3D human motion simulation model will be built for a true representation of the process of human motion,then the effectiveness of the attitude calculation method has been verifid.The experimental results show that the Dual-Quaternions method of attitude calculation for analysis of human motion parameters,which can obtain the trajectory simulation of joints to avoid singular point motion joint phenomenon,that sets foundations for further human motion capture.2.In order to solve the problem of the classification accuracy and efficiency in the process of motion capture,this paper proposes an improved Adaboost motion capture feature classification algorithm.The motion capture technique based on natural interaction will be used to collect motion data of the golf putting task behavior,including the kinematics parameters of the right arm and club head combined inertial motion capture system.The improved Adaboost classification feature algorithm has been used to classify the motion parameters of athletes hand joints.A strong classifier of the better weak classifiers formed can be selected adaptively recognition rate in the weak classifiers feature set,then it is from local optimal solution to obtain the global optimal solution.Experimental results show that the improved Adaboost feature classification algorithm can improve the recognition rate of motion capture recognition at 8%,and the accuracy is also improved by 10%to 15%.At the same time,the method of analysis of variance and statistical parameters are introduced to assess the success rate and the golf player attitude.The results show professional players have steadily putting in the process of putting and mastered appropriate strength of the putting,small fluctuate acceleration and higher success rate.3.For a golf putting movement characteristic,the relationship between the behavior with golf putting movement and EEG signals will be as the research object.This paper presents a statistical variance analysis method for the correlation of EEG signals with putting posture and successful and failure putts.Wireless inertial motion capture system and EEG acquisition system will be used to collect motion parameters and EEG signals of athletes simultaneously.EEG signals of putting process characteristics can be extracted,the change regularity of putting events and EEG power spectrum can be analyzed and the correlation model will be built.The ANOVA method can be used to analyze the variance of statistical putting process and fluctuate trend of the golfer EEG signals.At the same time,and the correlation between the success and failure with EEG signals will be analyzed.Exiperiment results show that the forehead(AF3)EEG Alpha rhythm is higher than that of the right prefrontal(AF4)potential signal during the process of putting.It shows that the athletes pay attention high concentration.In the correlation analysis of success rate and EEG signals,EEG Alpha rhythm of successful putting is higher than the presence of failure.It it proved that the visual attention and mental activity of athletes decrease after putting.4.For a driving around the turning left and right characteristics,the relationship between the behavior and EEG signals with driving behavior will be as the research object.This paper presents a study on the correlation between driving behavior and EEG signals,builds a connectivity model of EEG signals network.Driving behavior of motion parameters and the EEG signals will be collected,to analyze the relationship between the driver's arm motion attitude change parameters and EEG characteristics,prove the actual driving direction of the operation process of the wheel steering behavior caused by motor cortex event correlation(ERP)rhythms of EEG.CSP space filtering and WPD method will be used in EEG signals for feature extraction,which compared to the acceleration of the operating arm for the correlation research.The results show that turning left and right induced the C3?C4?CP3 and CP4 ERD/ERS phenomenon obviously.Thus,it can provide part based data of the accurately EEG electrical control for realize quantitative studies.