Electroencephalography Signal

Electroencephalography (EEG) signals, reflecting the brain's electrical activity, are central to understanding brain function and diagnosing neurological and psychiatric disorders. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional and recurrent neural networks, as well as other models such as Kolmogorov-Arnold Networks and transformers, to analyze EEG data for applications ranging from sleep apnea and Alzheimer's detection to classifying seizure types and decoding speech from brain activity. These advancements offer the potential for more accurate, objective, and accessible diagnostic tools and brain-computer interfaces, improving healthcare and assistive technologies.