Motor Imagery

Motor imagery (MI) research focuses on decoding brain activity associated with imagined movements, primarily using electroencephalography (EEG), to create brain-computer interfaces (BCIs). Current research emphasizes improving the accuracy and robustness of MI-BCI systems across different individuals and sessions, exploring advanced deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs), often incorporating techniques like transfer learning and attention mechanisms. These advancements hold significant potential for improving assistive technologies for individuals with motor impairments and expanding the applications of BCIs in various fields, including rehabilitation and robotics.