Research And Design Of EEG Control System For Lower Limb Exoskeleton Robot

In 2019,according to data released by the National Bureau of Statistics of China,the number of elderly people over 60 years of age reached 249.49 million,accounting for 17.9% of the total population,and the aging of our population has deepened.At present,the number of stroke patients in China has reached 70 million,and the new rate of stroke patients is increasing every year.90% of stroke patients can walk normally again through effective exercise.In addition,there are as many as 2 million new cases of lower limb dyskinesia caused by spinal cord injury and other reasons every year.A large number of elderly people with limited mobility,stroke patients,and patients with spinal cord injuries urgently need equipment or systems that can perform lower limb exercises.Based on the above problems,this thesis researched and developed a lower limb exoskeleton robot EEG control system.The system solves the lower limb exercise problems of stroke patients,spinal cord injury patients and the elderly through EEG control mode and manual control mode,respectively.The research content of this thesis is mainly divided into two parts:The first part is the research of EEG intention recognition.This thesis adopts the EEG acquisition method under the compound paradigm.The step function method is used for quality detection,and the EEG signals that do not meet the quality requirements are deleted by this method,and the EEG signals with higher signal noise are retained.Then the feature extraction method of wavelet packet decomposition coefficient and subspace energy is used.An intent recognition method combining Fisher classifier and BP neural network is proposed.Compared with the traditional method,the EEG intention recognition method has greatly improved accuracy.The second part is the design and development of the EEG control system of the lower exoskeleton robot.This part mainly includes the development of brain-computer interface software and the design of lower extremity exoskeleton robot.The brain-machine interface software was independently developed through the Visual Studio platform to achieve the acquisition and storage of EEG data.The method of calling the TeeChart control to display the EEG waveform in real time is proposed.By calling the matlab function,online recognition of EEG intentions is realized and can be converted into control instructions.In response to the special needs of patients with stroke and spinal cord injury,the lower limb exoskeleton robot designed in this thesis has a variety of motion modes such as brain control mode,jogmode and single leg mode.By communicating with the lower extremity exoskeleton robot through the brain-machine interface platform,the brain control mode of the lower extremity robot is realized,and the platform can obtain real-time feedback of motor angle and speed data in each mode to complete the monitoring of lower limb movement.In summary,this system provides a new and effective exercise method for the elderly with walking dysfunction and stroke patients,and also provides convenience for the patients' family members and medical staff.In addition,the development of this system also has important reference significance for the collaborative research of brain-computer interface technology and other control technologies.Therefore,the design and development of the system has important practical significance.