Research On Related Algorithms Of Motor Imagery Based Brain Computer Interface

Brain computer interface is a kind of interactive mode of information between brain and the outside world,which has been widely used in more and more fields.Research on the related algorithm has become an important topic.Motor-imagery EEG is a spontaneous signal.By taking a typical motor imagery EEG as the research object,this paper analyzed the shortcomings and improved methods of local mean decomposition and differential evolution.Then researched the feature extraction and recognition algorithms,optimal band selection algorithms also studied.Firstly,we analyzes the reason of the end effect problem in the local mean decomposition,and improves the method by using the method of self extension.The simulation results show that the proposed method can effectively reduce the impact of end effect.Aiming at the problem that the weak EEG signal is difficult to extract the effective features,this paper proposes a method of feature extraction based on multi entropy.The method processes motor imagery EEG with local mean decomposition,then obtained product function components.Calculating of the effective PF component's fuzzy entropy,multi-scale entropy,energy entropy.Three kinds of entropy as feature vector,and finally using learning vector quantization neural network for pattern recognition to realize the classification of motor imagery.The simulation experiment shows a better classification recognition rate,which proves that the method can extract weak motor-imagery EEG features.Secondly,the differential evolution algorithm may easily appear premature convergence,and difficultly set the best parameters.To resolve the above two problems,we proposed multi strategy method for mutation operator based on convex quadratic function and crossover exchange factor based on simgod function.By experimental analysis,we can verify the effectiveness of the proposed method.Finally,we used the improved differential evolution algorithm to obtain the optimize the frequency band to resolve the problem that the individual difference is difficult to self-adaption determine the optimal frequency band.In the frequency selection system,theEEG feature is extracted by the common spatial pattern,the classification accuracy is calculated by linear discriminant analysis,and use the differential evolution algorithm to search the best band.Experiments were carried out by using 3 datasets,and the results show that the proposed method is efficient and effective to select the optimal frequency band.And the method is also suitable for the optimal time selection and the optimal channel combination selection.