EEG Signal

Electroencephalography (EEG) signals, reflecting brain electrical activity, are central to understanding brain function and developing brain-computer interfaces (BCIs). Current research focuses on improving signal processing techniques, particularly artifact removal using methods like Empirical Mode Decomposition combined with machine learning, and developing advanced decoding methods using deep learning architectures such as transformers, variational autoencoders, and diffusion models for tasks ranging from visual decoding to emotion recognition and even imagined speech reconstruction. These advancements hold significant promise for improving the accuracy and reliability of EEG-based diagnostics, BCIs, and neuroergonomic studies, ultimately impacting healthcare, assistive technologies, and our understanding of the brain.