Retrieving visual content from electroencephalography (EEG) signals represents a challenging frontier in implicit multimedia analysis, yet it suffers from extreme modal heterogeneity. The high-dimensional, non-stationary noise in neural signals limits conventional point-to-point similarity measures in capturing complex semantic manifolds. To bridge this gap, we propose the Neuro-Image Geometric Contrastive Learning (NIGCL) framework. Departing from reliance solely on simple first-order similarity metrics, NIGCL employs a geometry-aware alignment mechanism rooted in manifold learning. Specifically, we incorporate a Geometric Area Contrastive Loss based on the Gram matrix determinant, which constrains the geometric area of cross-modal feature pairs to enforce intra-class compactness and mitigate the impact of orthogonal perturbations. This global constraint is complemented by a local dot-product objective for fine-grained consistency. Additionally, we propose Geometric Area Ranking (GaR) to replace standard ranking protocols, identifying semantically consistent images via the geometric area in high-dimensional spaces. Experiments on the THINGS-EEG dataset show that NIGCL achieves superior performance, attaining 31.2% Top-1 and 61.6% Top-5 accuracy in 200-way retrieval. Reconstruction evaluations further confirm that our geometrically-aligned representations significantly improve semantic fidelity over traditional methods. This framework offers a novel perspective on aligning highly heterogeneous multimedia data through explicit geometric constraints.

Major Depressive Disorder (MDD) is a common mental disorder, and making an early and accurate diagnosis is crucial for effective treatment. Functional Connectivity Network (FCN) constructed based on functional Magnetic Resonance Imaging (fMRI) have demonstrated the potential to reveal the mechanisms underlying brain abnormalities. Deep learning has been widely employed to extract features from FCN, but existing methods typically operate directly on the network, failing to fully exploit their deep information. Although graph coarsening techniques offer certain advantages in extracting the brain's complex structure, they may also result in the loss of critical information. To address this issue, we propose the Multi-Granularity Brain Networks Fusion (MGBNF) framework. MGBNF models brain networks through multi-granularity analysis and constructs combinatorial modules to enhance feature extraction. Finally, the Constrained Attention Pooling (CAP) mechanism is employed to achieve the effective integration of multi-channel features. In the feature extraction stage, the parameter sharing mechanism is introduced and applied to multiple channels to capture similar connectivity patterns between different channels while reducing the number of parameters. We validate the effectiveness of the MGBNF model on multiple classification tasks and various brain atlases. The results demonstrate that MGBNF outperforms baseline models in terms of classification performance. Ablation experiments further validate its effectiveness. In addition, we conducted a thorough analysis of the variability of different subtypes of MDD by multiple classification tasks, and the results support further clinical applications.

Emotion recognition from multimodal data remains challenging due to the semantic gap and temporal-spatial misalignment between EEG signals and facial expressions. To address this, we propose a cross-modal framework that integrates EEG and facial features via modality-guided semantic representation learning. Temporal features are extracted by stacked MAMBA-based blocks capturing long-range dependencies. A Cross-Modal Scaling and Shifting (CMSS) mechanism uses EEG features to refine and align facial representations, reducing modality discrepancies. The fused features pass through a GRUcontrolled Mixture-of-Experts (MoE-GRU) module, where a learnable gating network dynamically selects specialized Transformer experts. This combination effectively handles misalignment and enables dynamic feature fusion, enhancing recognition accuracy and robustness. Experiments on DEAP and MAHNOBHCI demonstrate state-of-the-art results, validating its real-world applicability in affective computing.

To tackle the challenge of effectively representing time-varying information in cross-subject EEG emotion recognition, we introduce Swin Transformer-Based Temporal-Channel Network (Swin-TCNet), a novel multi-scale neural network with parallel temporal pathways to enhance the extraction of dynamic temporal features. EEG signals are simultaneously processed through a Temporal Swin Transformer for 3D feature representation and a dynamic spatiotemporal convolutional layer with multi-head attention for extracting differential entropy-based channel features, which enhances channel learning while preserving temporal information. Swin-TCNet attains state-of-the-art performance, achieving 93.47% and 86.80% accuracy in cross-subject experiments on SEED and SEED-IV datasets, respectively, as substantiated by ablation studies. By leveraging temporal dynamics, this framework enhances the extraction of temporal variations and spatial information, leading to more robust and generalizable cross-subject emotion recognition.