EEG Data

Electroencephalography (EEG) data analysis aims to extract meaningful information about brain activity from scalp-recorded electrical signals, primarily for diagnostic and research purposes. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and graph neural networks (GNNs), along with techniques like self-supervised learning and domain adaptation to address challenges such as data variability and noise. These advancements are improving the accuracy and efficiency of EEG-based applications, including disease detection (e.g., Parkinson's, epilepsy, dementia), emotion recognition, and brain-computer interfaces, with a growing emphasis on model interpretability and robustness. The resulting insights hold significant potential for advancing neuroscience understanding and improving clinical practice.