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The field of spatial cognitive training and evaluation has rapidly evolved. However, the low learning motivation and engagement of the subjects hinder the widespread use of spatial cognitive training. This study designed a home-based spatial cognitive training and evaluation system (SCTES), which aimed to train subjects on spatial cognitive tasks for 20 days, and compared the brain activities before and after the training. This study also evaluated the feasibility of using a portable all-in-one prototype for cognitive training that combined a virtual reality (VR) head-mounted display with high-quality electroencephalogram (EEG) recording. During the course of training, the length of the navigation path and the distance between the starting position and the platform position revealed significant behavioral differences. In the testing sessions, the subjects showed significant behavioral differences in the time it took to complete the test task before and after training. After only four days of training, the subjects demonstrated significant differences in the Granger causality analysis (GCA) characteristics of brain regions in the ${\delta }$ , ${\theta }$ , ${\alpha }_{{{1}}}$ , ${\beta }_{{{2}}}$ , and ${\gamma }$ frequency bands of the EEG, as well as significant differences in the GCA of the EEG in the ${\beta }_{{{1}}}$ , ${\beta }_{{{2}}}$ , and ${\gamma }$ frequency bands between the two test sessions. The proposed SCTES used a compact and all-in-one form factor to train and evaluate spatial cognition and collect EEG signals and behavioral data simultaneously. The recorded EEG data can be used to quantitatively assess the efficacy of spatial training in patients with spatial cognitive impairments.

In order to improve the traditional common space pattern (CSP) algorithm pattern in EEG feature extraction, this study proposes a feature extraction method of EEG signals based on permutation conditional mutual information common space pattern (PCMICSP), which used the sum of the permutation condition mutual information matrices of each lead to replacing the mixed spatial covariance matrix in the traditional CSP algorithm, and its eigenvectors and eigenvalues are used to construct a new spatial filter. Then the spatial features in the different time domains and frequency domains are combined to construct the two-dimensional pixel map, Finally, a convolutional neural network (CNN) is used for binary classification. The EEG signals of 7 community elderly before and after spatial cognitive training in virtual reality (VR) scenes were used as the test data set. The average classification accuracy of the PCMICSP algorithm for pre-test and post-test EEG signals is 98%, which was higher than that of CSP based on CMI (conditional mutual information), CSP based on MI (mutual information), and traditional CSP in the combination of four frequency bands. Compared with the traditional CSP method, PCMICSP can be used as a more effective method to extract the spatial features of EEG signals. Therefore, this paper provides a new approach to solving the strict linear hypothesis of CSP and can be used as a valuable biomarker for the spatial cognitive evaluation of the elderly in the community.

Purpose This study aimed to calculate and analyze the differences in electroencephalogram (EEG) between major depression disorder (MDD) patients and healthy controls using the method of brainwave music to provide evidence of physiological parameters for clinical diagnosis of MDD. Methods We translated the 5-minute resting EEG data of 20 MDD patients and 20 healthy controls, which were collected by Peking University Sixth Hospital, into brainwave music; and calculated the pitch, volume, duration and corresponding scaling exponents to analyze the differences between MDD patients and healthy people. Then combined the analysis methods of nonlinear dynamics and brain network structure to verify the validity of the proposed method. Results The pitch, volume, and Lemple-Ziv complexity (LZC) in the frontal lobe of MDD patients were higher than those of healthy people. In the central, occipital, and temporal regions of the left hemisphere, the scaling exponents, degrees, and clustering coefficient of MDD patients were lower than those of healthy people (P

Artificial intelligence technology is the outcome of economic globalization. With the advent of the digital age, artificial intelligence plays an increasingly important role in various fields including the constantly updated and iterative healthcare and clinical service. Diabetes is a chronic non-communicable disease characterized by hyperglycemia. Many scholars are committed to researching diabetes prediction, intelligent diagnosis, early screening of complication and health management from an innovative perspective, so as to promote the health state of people. This paper mainly summarizes the application of artificial intelligence in diabetes research and clinical practice in recent years and puts forward future prospects for the intelligence and automation of diabetes diagnosis and treatment.