No abstract

Electroencephalogram (EEG) signals have emerged as an important tool for emotion research due to their objective reflection of real emotional states. Deep learning-based EEG emotion classification algorithms have made preliminary progress, but existing models struggle with capturing long-range dependence and integrating temporal, frequency, and spatial domain features to limit their classification ability. To address these challenges, this study proposes a Bi-branch Vision Transformerbased EEG emotion recognition model, Bi-ViTNet, that integrates spatial-temporal and spatial-frequency feature representations. Specifically, Bi-ViTNet is composed of spatial-frequency feature extraction branch and spatial-temporal feature extraction branch, which can fuse spatial-frequency-temporal features in a unified framework. Each branch is composed of Linear Embedding and Transformer Encoder, which is used to extract spatial-frequency features and spatial-temporal features. Finally, fusion and classification are performed by the Fusion and Classification layer. Experiments on SEED and SEED-IV datasets demonstrate that Bi-ViTNet outperforms state-of-the-art baselines.

In this paper, we have proposed a flexible noncontact crack-size measurement method that can realize binocular stereo vision measurement with only a single camera. On the premise that the camera’s intrinsic parameters have been accurately calibrated, we use a camera to collect the image of the crack from two directions. Then, we calculate the motion parameters using the collected images from the camera in different positions. In addition, Canny algorithm is used to extract the edge pixels of crack images. Finally, we establish the binocular stereo vision model for crack measurement according to the camera parameters, the motion parameters, and the edge information of crack images. Thus, we can measure the crack size through this model. Experimental results show that the measurement error is less than 5% under a distance of 2 meters, which can effectively prove the precision of the proposed method. In addition, our method only uses a single camera. Compared with the traditional binocular stereo vision method, this method is not only flexible but also more economical.