The field of brain signal processing and neuroimaging is rapidly advancing, with a focus on developing innovative methods for decoding and reconstructing visual neural representations. Recent research has explored the use of diffusion models, cross-attention mechanisms, and multimodal fusion techniques to improve the accuracy and biological plausibility of brain signal generation and reconstruction. Notably, the integration of textual information and dynamic balancing strategies has shown promise in enhancing semantic correspondence and alignment between different modalities. Furthermore, the development of new datasets and benchmarking frameworks has facilitated the evaluation and comparison of different approaches, driving progress in the field. Some noteworthy papers include: Diffusion-Based Image-to-Brain Signal Generation with Cross-Attention Mechanisms for Visual Prostheses, which proposes a novel framework for generating biologically plausible brain signals. DynaMind: Reconstructing Dynamic Visual Scenes from EEG by Aligning Temporal Dynamics and Multimodal Semantics to Guided Diffusion, which introduces a framework for reconstructing video from EEG signals with high fidelity and temporal coherence. RTGMFF: Enhanced fMRI-based Brain Disorder Diagnosis via ROI-driven Text Generation and Multimodal Feature Fusion, which presents a framework for brain disorder diagnosis using fMRI and textual information. Decoding Visual Neural Representations by Multimodal with Dynamic Balancing, which proposes a framework for decoding visual neural representations using EEG, image, and text data. A Dataset Generation Scheme Based on Video2EEG-SPGN-Diffusion for SEED-VD, which introduces a framework for generating a multimodal dataset of EEG signals conditioned on video stimuli. ArtifactGen: Benchmarking WGAN-GP vs Diffusion for Label-Aware EEG Artifact Synthesis, which compares the performance of WGAN-GP and diffusion models for synthesizing realistic EEG artifacts. Symmetry Interactive Transformer with CNN Framework for Diagnosis of Alzheimer's Disease Using Structural MRI, which proposes a framework for diagnosing Alzheimer's disease using structural MRI and a symmetry interactive transformer.