Spatial Cognitive Training And Evaluation Method For The Elderly In The Community By Combining Brain-Computer Interface With Virtual Reality

At present,China is gradually entering an aging society,and more and more researchers at home and abroad are paying attention to improving the cognitive ability of the elderly,especially the spatial cognitive ability to reduce the incidence of Alzheimer’s disease.Electroencephalogram(EEG)analysis,as a highly effective way to evaluate changes in the cognitive nerve activity of the brain,has also made great progress after the introduction of deep learning algorithms.Therefore,this article conducts spatial cognitive training experiments on the elderly in a scientific and effective way,and evaluates the changes of cognitive neural activities of individuals when performing tasks in the experiment from multiple angles of behavior data statistics and EEG signal analysis.First,this article uses Brain Computer Interface(BCI)and Virtual Reality(VR)technology to conduct spatial cognitive training experiments for the elderly in the community.The experiment designed spatial cognitive training and testing tasks,and collected the subject’s EEG signals and behavior data,and tested three spatial cognitive scales before and after the training.Then statistical analysis of training time,correct number of turns,spatial cognitive scale,and then use Power Spectral Density,Support Vector Machine and Extreme Learning Machine method to extract and classify the sample data,and the experimental training effect was effectively evaluated.Secondly,in order to further explore the relationship between different brain regions and different frequency bands of EEG signals in the field of spatial cognition,a method of Weighted Multivariate Permutation Conditional Mutual Information(WMPCMI)is proposed.In this method,the frequency domain features and mutual information features are fused in a weighted form to obtain a WMPCMI eigenvalue matrix for a certain frequency band.Then combine the WMPCMI features of the three frequency bands and use convolutional neural network to classify,in order to effectively evaluate the difference of spatial cognitive EEG signals before and after training.Finally,the above experimental research is analyzed,and the spatial cognitive training effect of the subjects is evaluated from two aspects of behavior data statistics and EEG signal analysis.In addition,by comparing various feature extraction methods and WMPCMI feature extraction in the convolutional neural network model classification of various indicators,the superiority of WMPCMI in spatial cognitive EEG signal analysis is verified.