Spatial Cognition Assessment And Recognition Method Based On Brain-Computer Interface In Virtual Scene

With the rise of artificial intelligence,brain neuroscience research using Brain Computer Interface(BCI)as a bridge is rapidly unfolding.Among them,the analysis of spatial cognitive EEG signals in virtual reality has become a research hotspot in this field.The changes of EEG signals before and after spatial cognitive training can effectively evaluate the effect of cognitive ability training.At present,a lot of progress has been made in the research of spatial cognitive EEG signals,mainly including the feature extraction of EEG signals and subsequent data classification.However,there are still some shortcomings,which mainly reflected in the calculation of the coupling feature strength between different channels without considering the influence of the spatial position of other channels.In addition,it is also a major difficulty in this field to explore a classification model of EEG feature data with stronger robustness and better performance.Based on this,this article proposed a new EEG coupling feature extraction method and EEG signal classification model combining the current research status at home and abroad.Firstly,this article proposed an EEG signal feature extraction algorithm based on the Multivariate Conditional Mutual Information Common Spatial Pattern(MCMICSP),combining the theory of mutual information.This is due to the fact that the traditional CSP algorithm input EEG signal is required to show strict linear correlation,it is not suitable for the EEG data of this experiment.Therefore,the covariance matrix in the traditional co-space algorithm is replaced by the multi-dimensional conditional mutual information coupling matrix,which not only considers the influence of other EEG channels on the coupling feature,but also constructs the spatial feature filter is according to the linear correlation degree of EEG signal,which further makes up for the shortcomings of the existing feature extraction methods.Secondly,this paper proposed a multiple scale dense fusion convolutional neural network(Multiple Scale Dense Fusion Convolutional Neural Network,MSDFCNN).This is due to the fact that traditional CNN convolution kernel is single and easy to lose effective feature information,different scale convolution kernels are introduced to effectively reduce the feature loss of EEG signal.In view of the relatively small number of samples studied in this experiment,the dense network method is introduced to achieve feature reuse while reducing gradient dispersion;and the adaptive gradient stochastic descent algorithm is used to optimize the process of the proposed classification algorithm.Finally,the experiment and result analysis of the above content are carried out.In order to verify the effectiveness of the two proposed algorithms,this study selected the EEG data before and after spatial cognitive training in the virtual reality as the data set,and carried out comparative experiments with the original feature extraction and classification algorithms.The results show that the feature extraction algorithm proposed in this study can more effectively evaluate the effect of spatial cognition training;the proposed classification algorithm has higher accuracy and stronger generalization ability in the classification of EEG coupling features.