Brain Dynamic

Brain dynamics research focuses on understanding the complex temporal and spatial patterns of neural activity underlying cognition and behavior. Current investigations utilize advanced machine learning models, including graph neural networks, transformers (like Swin Transformers and Continuous Spatiotemporal Transformers), and novel approaches such as Neural Integro-Differential Equations and Masked Autoencoders, to analyze diverse neuroimaging data (EEG, fMRI). These analyses aim to improve the accuracy of disease diagnosis (e.g., ADHD, epilepsy), predict cognitive states (e.g., drowsiness, working memory load), and ultimately reveal the causal mechanisms driving brain function, paving the way for more effective clinical interventions and brain-computer interfaces.