EEG Regression

EEG regression aims to predict continuous variables from electroencephalography (EEG) data, primarily for applications in brain-computer interfaces (BCIs) and neuroscience research. Current research emphasizes improving the speed and accuracy of these predictions, focusing on model architectures like Vision Transformers (ViTs), Temporal Convolutional Networks (TCNs), and hybrid approaches that combine these with other techniques such as knowledge distillation and graph attention mechanisms. These advancements are crucial for developing more robust and efficient BCIs, enabling real-time applications and enhancing our understanding of brain dynamics.