Due to the individual differences controlling brain-computer interfaces (BCIs), the applicability and accuracy of BCIs based on motor imagery (MI-BCIs) are limited. To improve the performance of BCIs, this article examined the effect of transcranial electrical stimulation (tES) on brain activity during MI. This article designed an experimental paradigm that combines tES and MI and examined the effects of tES based on the measurements of electroencephalogram (EEG) features in MI processing, including the power spectral density (PSD) and dynamic event-related desynchronization (ERD). Finally, we investigated the effect of tES on the accuracy of MI classification using linear discriminant analysis (LDA). The results showed that the ERD of the μ and β rhythms in the left-hand MI task was enhanced after electrical stimulation with a significant effect in the tDCS group. The average classification accuracy of the transcranial alternating current stimulation (tACS) group and transcranial direct current stimulation (tDCS) group (88.19% and 89.93% respectively) were improved significantly compared to the pre-and pseudo stimulation groups. These findings indicated that tES can improve the performance and applicability of BCI and that tDCS was a potential approach in regulating brain activity and enhancing valid features during noninvasive MI-BCI processing.

Against the background of the aging trend in China, construction and regeneration strategies for an aging-friendly built environment are becoming common, led by urban governments, and public street spaces are the focus of these strategies. Exploring such planning and design strategies can help to improve the social welfare of the aging population and meet their diverse needs. Thus, this paper, through analyzing the determinants of the elderly’s needs, examines the relationship between spatial perception and street form, using Shanghai, in China, as a case study. This study contributes to the current literature in two ways: first, it constitutes the first attempt to build a needs hierarchy for aging people in a Chinese developed city; second, our statistical analysis involves large-scale population surveys, which helps us to comprehensively and deeply understand the impact of detailed street forms on the elderly’s various spatial perceptions. Our results indicate that the renovation of street space in different areas of cities can be improved by the control of street form, to meet the diverse needs of the local aging group.

Currently, spatiotemporal features are embraced by most deep learning approaches for human action detection in videos, however, they neglect the important features in frequency domain. In this work, we propose an end-to-end network that considers the time and frequency features simultaneously, named TFNet. TFNet holds two branches, one is time branch formed of three-dimensional convolutional neural network(3D-CNN), which takes the image sequence as input to extract time features; and the other is frequency branch, extracting frequency features through two-dimensional convolutional neural network(2D-CNN) from DCT coefficients. Finally, to obtain the action patterns, these two features are deeply fused under the attention mechanism. Experimental results on the JHMDB51-21 and UCF101-24 datasets demonstrate that our approach achieves remarkable performance for frame-mAP.

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