Research On Active Motor Nerve Rehabilitation Technology Based On Condition Monitoring

With the aging aggravation of the society at present,stroke chances in the middle-aged and elderly in China continue to increase.Since its after-treatment rehabilitation is particularly important,the relevant issue has attracted great attention from all walks of life.Routine rehabilitation trainings usually require that patients perform under the guidance of rehabilitation professionals,which are passive for the patients and involve complex operations and long cycles,so patients’ willingness of active participation is weak.Motor imagery（MI）therapy,also known as active motor nerve rehabilitation technology,has become a research hotspot in the field of rehabilitation and brain computer interface（BCI）.However,there are also many problems: individual differences among patients lead to distinct implementation effects of MI;At the same time,the treatment effect depends heavily on subjects’ personal status;In addition,patients lack in active participation,and it is difficult to keep their involvement,so appropriate mobilization is necessary.Because there are many indicators of personal state monitoring,such as fatigue,emotion and attention,since enhancing attention can have great impact on improving the effect of motor imagery,and attention can also reflect the concentration state,this thesis selected attention state as the indicator of state monitoring.In order to better solve the problems in motor imagery therapy and state monitoring,as well as improve the rehabilitation effect,this paper designs a motor imagination rehabilitation system based on monitoring attention state,carries out the experiment for the purpose of improving subjects’ attention state,and constructs the relevant assessment system.The main research contents of this paper are as follows:Firstly,the off-line experimental system is designed,the hardware equipment is connected and tested,then the experimental paradigm is designed.On this basis,the experiment is carried out,and the experimental data are preprocessed first,and then the multi-angle feature extraction and analysis are performed through time domain,frequency domain,time-frequency domain,paired t-test and alpha modulation index（AMI）,which verifies the effectiveness of AMI indicators in evaluating attention state,confirms the optimal selection of brain regions and leads;Then,an online experimental system is designed to feed back the subjects’ motor imagery effect and attention state in real time through the visualization of theta / beta ratio（TBR）index,wavelet energy,power spectral density（PSD）and EEG topographic map;Finally,by comparing with the control data,the system can evaluate the subjects’ attention state,and control the movement of the manipulator to intuitively feed back the results to the subjects.