EEG Reconstruction

EEG reconstruction aims to accurately recover brain activity signals from noisy or incomplete electroencephalography (EEG) recordings. Current research heavily utilizes deep learning, particularly convolutional neural networks and variational autoencoders, often incorporating techniques like dynamic time warping to improve reconstruction fidelity and handle inter-subject variability. These advancements enable improved artifact removal, leading to more reliable EEG data for analysis and potentially enhancing the accuracy of brain-computer interfaces and other neurotechnology applications. Furthermore, self-supervised learning approaches are emerging, focusing on cross-modal alignment between EEG and visual stimuli to achieve more robust and generalizable reconstruction.