Brain-computer interface technology (BCI) enables users to directly control external devices by establishing an information transmission path between the brain and external devices. Brain-computer interfaces based on the motor imagination paradigm have also begun to enter various fields. Therefore, the research on the brain-computer interface encoding and decoding algorithm of the motor imagination paradigm is particularly important. This paper proposes a model based on attention mechanism CBAM and EEGNet to classify motor imagination electroencephalogram signals (MI-EEG), and verified it on a public data set. Compared with a single EEGNet model, it improved by 3.7%, which is 8.1% higher than the traditional FBCSP model. The experimental results show the effectiveness of the new CBAM-EEGNet model on the four classification tasks of motor imagery.
Polyp segmentation has consistently been a difficult task because of the varying sizes of polyps and the significant intrinsic similarity between polyps and the surrounding tissues. To address the above problems, a contextual feature aggregation polyp segmentation algorithm combining Pyramid Vision Transformer and convolution (CFA-PVT) is proposed. Firstly, the Pyramid Vision Transformer is used to extract image global features, and the stage bridging module(SBM) is employed to enhance the ability of the network to handle polyp details and aggregate high-level polyp features. Subsequently, a feature enhancement module (FEM) is used to explore shallow polyp information. Finally, cross-layer feature fusion is performed by a global adaptive module (GAM) to realize feature interaction. This algorithm is evaluated on the CVC-ClinicDB and Kvasir-SEG datasets and further tested for generalization capability on the CVC-ColonDB dataset. The results demonstrate that the proposed method effectively segments colorectal polyp images, offering a new approach to diagnosing colorectal polyps.
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