Brain Encoding

Brain encoding research aims to understand how the brain represents information from various stimuli (images, language, sounds) by modeling the relationship between stimuli and resulting brain activity patterns, typically measured using fMRI. Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs), transformers (both unimodal and multimodal), and ridge regression, often focusing on improving model generalization, scalability, and the integration of multimodal data (e.g., combining visual and linguistic information). These advancements are crucial for improving brain-computer interfaces, diagnosing neurological conditions, and furthering our understanding of cognitive processes, offering insights into how the brain processes and represents information across different modalities.