Objective To reflect the correlation between social support and mental health of the aged through the Pearson correlation coefficient. Methods Databases including PubMed, SpringerLink, EMbase, The Cochrane Library, VIP, WanFang Data and CNKI were searched from inception to October, 2011 to collect literature on the correlation between social support and mental health of the aged. The studies were screened according to the inclusion and exclusion criteria. After extracting data and assessing the quality of the included studies, meta-analysis was conducted using RevMan 5.0 software. Results Of the 2 396 identified studies, 4 studies were included. The results showed that 4 studies were not high in the overall quality. The total score of social support of the elderly and its three dimensions were related to mental health. Among 9 factors associated with mental health, somatization, depression and anxiety were weakly correlated to the objective support while the others were extremely weakly correlated. Anxiety and phobic anxiety were weakly correlated to the subjective support while the others were extremely weakly correlated. Phobic anxiety was weakly correlated to the utilizing degree while the others were extremely weakly correlated. Somatization, anxiety and phobic anxiety were weakly correlated to the total score of social support while the others were extremely weakly correlated. Conclusion Social support probably improves mental health of the aged to some extent.
Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects’ optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.