Brain Electroencephalography

Electroencephalography (EEG) measures brain electrical activity, offering a non-invasive method for studying brain function and diagnosing neurological and psychological conditions. Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and variational autoencoders (VAEs), often coupled with techniques like transfer learning, self-supervised learning, and feature fusion to improve accuracy and address challenges such as data scarcity and noise. These advancements are enabling more precise diagnoses of conditions like Alzheimer's disease, epilepsy, and anxiety disorders, as well as applications in brain-computer interfaces and real-time monitoring of cognitive states like fatigue and workload. The field is also focusing on improving the interpretability of these complex models to enhance clinical utility and trust.