Brain Computer Interface Paradigm

Brain-computer interfaces (BCIs) aim to establish direct communication pathways between the brain and external devices, primarily using electroencephalography (EEG) signals. Current research heavily focuses on improving the accuracy and generalizability of BCIs across diverse paradigms (e.g., motor imagery, P300, SSVEP) through advanced machine learning techniques, including Riemannian geometry methods, deep learning architectures (like convolutional neural networks and JEPAs), and attention mechanisms. These advancements aim to minimize the need for extensive subject-specific calibration, enhance cross-dataset transferability, and improve the interpretability of BCI models, ultimately leading to more robust and practical BCI systems for various applications.