The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-means clustering, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction. Tackling these limitations, this study introduces a Global Map Dissimilarity (GMD)-driven density canopy K-means clustering algorithm. This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data. Utilizing this advanced algorithm, the study analyzes the Motor Imagery (MI) dataset from the GigaScience database, GigaDB. The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions, with microstate C showing superior performance in all task states. During imagined movement, microstate A was significantly enhanced. Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method, with an average Calinski-Harabasz Index (CHI) of 35517.29 and a Davis-Bouldin Index (DBI) average of 2.57. Furthermore, an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement. By utilizing the extracted microstate sequence parameters as features, the improved algorithm achieved a classification accuracy of 98.41% in EEG signal categorization for motor imagery. A performance of 78.183% accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset. These results demonstrate the potential of the advanced algorithm in microstate analysis, offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.

Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methods have been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies based on Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesion region. However, over-reliance on prior information may ignore the background information that is helpful for diagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset. Consequently, the Prior Difference Guidance Network (PDGNet) is proposed, merging decoupled lesion and background information via the Prior Normalization Fusion (PNF) strategy and the Feature Difference Guidance (FDG) module, to direct the model to concentrate on beneficial regions for diagnosis. Extensive experiments in the clinical dataset demonstrate that the proposed method achieves promising diagnosis performance: PDGNets based on conventional networks record an ACC (Accuracy) and AUC (Area Under the Curve) of 87.50% and 89.98%, marking improvements of 8.19% and 7.64% over the prior-free benchmark. Compared to lesion-focused benchmarks, the uplift is 6.14% and 6.02%. PDGNets based on advanced networks reach an ACC and AUC of 89.77% and 92.80%. The study underscores the potential of harnessing background information in medical image diagnosis, suggesting a more holistic view for future research.