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Event-Related Desynchronization (ERD) or Electroencephalogram (EEG) wavelet is essential for motor imagery (MI) classification and BMI (Brain–Machine Interface) application. However, it is difficult to recognize multiple tasks for non-trained subjects that are indispensable for the complexities of the task or the uncertainties in the environment. The subject-independent scenario, where an inter-subject trained model can be directly applied to new users without precalibration, is particularly desired. Therefore, this paper focuses on an effective attention mechanism which can be applied to a subject-independent set to learn EEG motor imagery features. Firstly, a custom form of sequence inputs with spatial and temporal dimensions is adopted for dual headed attention via deep convolution net (DHDANet). Secondly, DHDANet simultaneously learns temporal and spacial features. The features of spacial attention on each input head are divided into two parts for spatial attentional learning subsequently. The proposed model is validated based on the EEG-MI signals collected from 54 subjects in two sessions with 200 trials in each sessions. The classification of left and right hand motor imagery in this paper achieves an average accuracy of 75.52%, a significant improvement compared to state-of-the-art methods. In addition, the visualization of the frequency analysis method demonstrates that the temporal-convolution and spectral-attention is capable of identifying the ERD for EEG-MI. The proposed machine learning structure enables cross-session and cross-subject classification and makes significant progress in the BMI transfer learning problem.

Today's economic development is focused on abilities, and the rivalry for talents is becoming increasingly evident. This paper identifies fifteen key influencing indicators of talent attraction of nine cities in Fujian Province. The principal component analysis (PCA) approach is then utilized to examine the talent attractiveness of nine cities in Fujian Province in depth. The results show that the ranking of talent attractiveness of nine cities in Fujian Province is as follows: Xiamen, Fuzhou, Putian, Quanzhou, Ningde, Sanming, Longyan, Zhangzhou, and Nanping. It shows that the cities with higher economy are more attractive to talents. Talents have the potential to propel the economy forward. On the contrary, the growth of the economy will draw an influx of talent.