EEG Classification

EEG classification aims to categorize brain activity patterns recorded via electroencephalography, primarily for applications in brain-computer interfaces and medical diagnostics. Current research emphasizes improving classification accuracy and generalizability across individuals by employing advanced deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs), often combined with innovative data augmentation and preprocessing techniques. These advancements hold significant promise for enhancing the reliability and clinical utility of EEG-based systems, leading to improved diagnostic tools and more effective brain-computer interfaces.