EEG Super Resolution

EEG super-resolution aims to enhance the spatial and temporal resolution of electroencephalogram (EEG) recordings, improving the quality of data obtained from relatively low-density electrode arrays. Current research focuses on deep learning approaches, particularly convolutional autoencoders and transformers, to reconstruct high-resolution EEG signals from low-resolution inputs, often incorporating techniques to address noise and artifacts. These advancements hold significant promise for improving the accuracy of clinical diagnoses, such as epilepsy localization, and enabling more sophisticated analyses in neuroscience research by providing higher-fidelity EEG data from more accessible and cost-effective acquisition methods.